Fuzzy Information Processing ebook

  Guilherme A. Barreto (Eds.)

  Ricardo Coelho

Communications in Computer and Information Science 831

Fuzzy Information Processing 37th Conference of the North American Fuzzy Information Processing Society, NAFIPS 2018 Fortaleza, Brazil, July 4–6, 2018 Proceedings


in Computer and Information Science 831

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  • Guilherme A. Barreto Ricardo Coelho (Eds.)

  Fuzzy Information Processing

37th Conference of the North American Fuzzy

Information Processing Society, NAFIPS 2018 Fortaleza, Brazil, July 4–6, 2018 Proceedings Editors Guilherme A. Barreto Ricardo Department of Teleinformatics Engineering Department of Statistics & Applied Federal University of Ceará Mathematics Fortaleza, Ceará Federal University of Ceará Brazil Fortaleza, Ceará


ISSN 1865-0929

  ISSN 1865-0937 (electronic) Communications in Computer and Information Science

ISBN 978-3-319-95311-3

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  In 1965, Lofti Asker Zadeh published the seminal paper “Fuzzy Sets” (Information and Control, 8, 338–353), which describe the first ideas about a formal mathematical modelling intended to bridge the gap between classic binary modelling and the sub- jective way that humans relate to day-to-day situations. Despite these ideas being ambitious, this preliminary work inspired many researchers around the world and today his ideas are found in almost all branches of science. According to the website Google Scholar, this seminal paper has been cited in more than 100,000 scholarly works, and many consumer products and software have been built based on its mathematical concepts. Unfortunately, Professor Zadeh died in September 2017, and this book is a modest tribute to the generous, gentle continuous and always friendly support that the authors received over the years from Professor Lofti A. Zadeh.

  It can be noted that the research field of fuzzy sets and systems has undergone tremendous growth since 1965. This growth is in no small measure the result of the emergence of some important scientific societies in North America (North American Fuzzy Information Processing Society – NAFIPS – and IEEE Computational Intelli- gence Society – IEEE CIS), Europe (European Society for Fuzzy Logic and Tech-

  • – JSFTII), and South America, especially in Brazil, (Brazilian Society of Automatics – nology – EUSFLAT), Asia (Japan Society for Fuzzy Theory and Intelligent Informatics SBA – and Brazilian Society of Computational and Applied Mathematics – SBMAC). – IFSA). These societies promote scientific events in order to spread the state of the art, There is also a transnational scientific society (International Fuzzy Systems Association its applications, and technological advances. A quick search in the Scopus database gives an idea of the number of published articles about fuzzy sets and systems. By dividing time from 1965 until today into four periods, we obtain the following: (a) 4,754 published papers until 1990; (b) 27,773 published papers from 1991 to 2000; (c) 93,012 from 2001 to 2010; and (d) 105,604 from 2011 to the current date (May 2018). This search was made by using the words “Fuzzy Sets” or “Fuzzy Systems” or “Fuzzy Logic” as title, abstract, or keywords.

  Among the societies mentioned, NAFIPS is the premier fuzzy society in North America, which was founded in 1981. The purpose of NAFIPS is “the promotion of the scientific study of, the development of an educational institution for the instruction in, and the dissemination of educational materials in the public interest including, but not limited to, theories and applications of fuzzy sets through publications, lectures, sci- entific meetings, or otherwise.” In this role, we understand the importance and necessity of developing a strong intellectual base and encouraging new and innovative applications. In addition, we recognize our leading role in promoting interactions and technology transfer to other national and international organizations so as to bring the benefits of this technology to North America and the world. The scientific event organized by the NAFIPS has been contributing for more than 30 editions to the VI Preface

  growth of the number of articles published in the fuzzy sets and systems field. The first edition took place in the city of Logan, Utah, USA, in 1982, and it is held annually. enthusiasts of fuzzy thinking beyond the borders of North American countries. The 37th North American Fuzzy Information Processing Society Annual Conference (NAFIPS 2018) was held during July 4–6, 2018, in the beautiful city of Fortaleza, capital of the state of Ceará, located on the sunny northeast coast of Brazil. This event was held simultaneously with the 5th Brazilian Congress on Fuzzy Systems (CBSF 2018), bringing together researchers, engineers, and practitioners to share and present the latest achievements and innovations in the area of fuzzy information processing, to discuss thought-provoking developments and challenges, and to consider potential future directions. Bearing this in mind, the NAFIPS 2018 meeting was the first edition of the meeting to be organized outside the USA, Canada, and Mexico. NAFIPS 2018 had an international Program Committee including researchers from industry and academia worldwide.

  The organization of NAFIPS 2018 and CBSF 2018 was the result of a joint action of the Brazilian Computational Intelligence Society (SBIC), the Brazilian Society of Computational and Applied Mathematics (SBMAC), the Federal University of Ceará (UFC), and the Brazilian funding agencies CAPES, process 88887.155510/2017-00, and CNPq, project 407666/2017-6, in addition to the executive boards of NAFIPS and CBSF.

  This book is a collection of high-quality papers ranging over a large spectrum of topics, including theory and applications of fuzzy numbers and sets, fuzzy logic, fuzzy inference systems, fuzzy clustering, fuzzy pattern classification, neuro-fuzzy systems, fuzzy control systems, fuzzy modeling, fuzzy mathematical morphology, fuzzy dynamical systems, time series forecasting, and making decision under uncertainty.

  We received 73 submissions from 11 countries, from which 54 papers were accepted. The authors were from Brazil, Chile, Colombia, Czech Republic, India, Iran, Mexico, Romania, Spain, Turkey, and the USA. Each submitted paper was reviewed by at least three independent referees. The acceptance/rejection decision used the following criteria: every paper with two positive reviews was accepted, and those with two negative reviews were rejected. Borderline papers, those with one positive and one negative review, were analyzed carefully by the conference chairs in order to evaluate the reasons given for acceptance or rejection. Our final decision on these submissions took into account mainly the potential of each paper to foster fruitful discussions and the future development of the research on the theory and applications of fuzzy sets and systems in Brazil and, for extension, in the whole of Latin America.

  We are enormously grateful to all reviewers for their goodwill in cooperating for the success of the aforementioned events. We very much appreciate their willingness for hard work and prompt feedback, which certainly guaranteed the high quality of the technical program.

  We wish NAFIPS a long life. And we wish a long life for the Brazilian community, who organizes CBSF, with which we share this mutual congress. June 2018

  Guilherme A. Barreto Ricardo Coelho Organization General Co-chairs

  Guilherme Barreto Federal University of Ceará, Brazil Ricardo Coelho Federal University of Ceará, Brazil

  Organizing Committee

  Fernando Gomide University of Campinas, Brazil Guilherme Barreto Federal University of Ceará, Brazil Laecio Carvalho de Barros University of Campinas, Brazil Patricia Melin Tijuana Institute of Technology, Mexico Ricardo Coelho Federal University of Ceará, Brazil Weldon Lodwick University of Colorado Denver, USA Centro Acadêmico de CAMI

  Matemática Industrial

  Web Masters

  Felipe Albuquerque Federal University of Ceará, Brazil Francisco Yuri Martins Federal University of Ceará, Brazil

  NAFIPS Officers

  Patricia Melin (President) Martine Ceberio (President-Elect) Christian Servin (Treasurer) Valerie Cross (Secretary)

  NAFIPS Board of Directors

  Ildar Batyrshin Ricardo Coelho Martine De Cock Scott Dick Juan Carlos Figueroa Garcia Weldon A. Lodwick Marek Reformat Shahnaz Shahbazova Mark Wierman Dongrui Wu Program Committee

  Giovanni Acampora University of Naples Federico II, Italy Plamen Angelov Lancaster University, UK Krassimir Atanassov Bulgarian Academy of Science, Bulgaria Adrian I. Ban University of Oradea, Romania Guilherme Barreto Universidade Federal do Ceará, Brazil Laecio Carvalho de Barros University of Campinas, Brazil Ildar Batyrshin Instituto Politécnico Nacional, Mexico Fernando Bobillo University of Zaragoza, Spain Giovanni Bortolan Institute of Neuroscience, IN-CNR, Italy Tadeusz Burczynski Institute of Fundamental Technological Research,

  Poland João Paulo Carvalho Instituto Superior Tecnico/INESC-ID, Portugal Oscar Castillo Tijuana Institute of Technology, Mexico Martine Ceberio University of Texas at El Paso, USA Wojciech Cholewa Silesian University of Technology, Poland Ricardo Coelho Universidade Federal do Ceará, Brazil Lucian Coroianu University of Oradea, Romania Valerie Cross Miami University, USA Bernard de Baets Ghent University, Belgium Didier Dubois Université Paul Sabatier, France Robert Fuller Óbuda University, Hungary Takeshi Furuhashi Nagoya University, Japan Fernando Gomide University of Campinas, Brazil Wladyslaw Homenda Warsaw University of Technology, Poland Sungshin Kim Pusan National University, South Korea Peter Klement Johannes Kepler University, Austria Vladik Kreinovich University of Texas at El Paso, USA Jonathan Lee National Central University, Taiwan Weldon A. Lodwick University of Colorado Denver, USA Francesco Marcelloni University of Pisa, Italy Radko Mesiar Slovak University of Technology, Slovakia Vesa Niskanen University of Helsinki, Finland Fabrício Nogueira Universidade Federal do Ceará, Brazil Vilem Novak University of Ostrava, Czech Republic Reinaldo Martinez Palhares Federal University of Minas Gerais, Brazil Irina Perfilieva

  University of Ostrava, Czech Republic Henri Prade Université Paul Sabatier, France Radu Emil Precup Politehnica University of Timisoara, Romania Alireza Sadeghian Ryerson University, Canada Yabin Shao Northwest University for Nationalities, China Andrzej Skowron University of Warsaw, Poland Umberto Straccia Consiglio Nazionale delle Ricerche, ISTI-CNR, Italy Ricardo Tanscheit Pontifícia Universidade Católica do Rio de Janeiro,


  VIII Organization



  Jose Luis Verdegay University of Granada, Spain Yiyu Yao University of Regina, Canada Fusheng Yu Beijing Normal University, China Slawomir Zadrozny Polish Academy of Sciences, Poland M. H. Fazel Zarandi Amirkabir University of Technology, Iran Guangquan Zhang University of Technology Sydney, Australia Hans J. Zimmermann RWTH Aachen University, Germany

  Additional Reviewers

  João Fernando Alcântara Jose Manuel Soto Hidalgo Aluizio Araújo Daniel Leite Rodrigo Araújo Adi Lin Romis Attux José Everardo Bessa Maia Iury Bessa Sebastia Massanet Arthur Braga Ajalmar Rêgo da Rocha Neto Luiz Cordovil Rudini Sampaio Pedro Coutinho Peter Sussner Alexandre Evsukoff George Thé Carmelo J.A. Bastos Filho Marcos Eduardo Valle Heriberto Román Flores Bin Wang João Paulo Pordeus Gomes Zhen Zhang



  . . .

   Solomon Gebreyohannes, Ali Karimoddini, Abdollah Homaifar, and Albert Esterline . . .

   Paulo Vitor de Campos Souza and Luiz Carlos Bambirra Torres . . .

  . . .

   Alisson Porto and Fernando Gomide . . .

   Laisa Ribeiro de Sá, José Carlos da Silva Melo, Jordana de Almeida Nogueira, and Ronei Marcos de Moraes . . .

   Afonso B. L. Neto, João P. B. Andrade, Tibério C. J. Loureiro, Gustavo A. L. de Campos, and Marcial P. Fernandez

  . . .

   Jiří Močkoř . . .

   Vinícius F. Wasques, Estevão Esmi, Laécio C. Barros, and Peter Sussner . . .

   Fabián Castiblanco, Camilo Franco, Javier Montero, and J. Tinguaro Rodríguez

  Francielle Santo Pedro, Laécio Carvalho de Barros, and Estevão Esmi


  Renato Dilli, Amanda Argou, Renata Reiser, and Adenauer Yamin

  Nilmara J. B. Pinto, Vinícius F. Wasques, Estevão Esmi, and Laécio C. Barros

  Camila Alves Dias, Jéssica C. S. Bueno, Eduardo N. Borges, Silvia S. C. Botelho, Graçaliz Pereira Dimuro, Giancarlo Lucca, Javier Fernandéz, Humberto Bustince, and Paulo Lilles Jorge Drews Junior


  Cedric Marco-Detchart, Graçaliz Pereira Dimuro, Mikel Sesma-Sara, Aitor Castillo-Lopez, Javier Fernandez, and Humberto Bustince

   Eduardo Palmeira, Benjamín Bedregal, and Rogério R. Vargas


  Henrique Viana and João Alcântara

  Abner Brito, Laécio Barros, Estevão Laureano, Fábio Bertato, and Marcelo Coniglio

  Lucas Agostini, Samuel Feitosa, Anderson Avila, Renata Reiser, André DuBois, and Maurício Pilla . . .

   Lidiane Costa, Mônica Matzenauer, Adenauer Yamin, Renata Reiser, and Benjamín Bedregal

   Valerie Cross and Shangye Chen

  XII Contents

   Marcelo Mafalda, Daniel Welfer, Marco Antônio De Souza Leite Cuadros, and Daniel Fernando Tello Gamarra

   Jairo Andres Perdomo-Tovar, Eiber Arley Galindo-Arevalo, and Juan Carlos Figueroa-García

   Ivan Mezzomo, Benjamín Bedregal, and Thadeu Milfont


  Mateus Sangalli and Marcos Eduardo Valle

  Aline Cristina de Souza and Marcos Eduardo Valle

  Jocivania Pinheiro, Benjamin Bedregal, Regivan Santiago, Helida Santos, and Graçaliz Pereira Dimuro

   Jocivania Pinheiro, Rui Paiva, and Regivan Santiago

   Érico Augusto Nunes Pinto, Leizer Schnitman, and Ricardo A. Reis


  Danúbia Soares Pires and Ginalber Luiz de Oliveira Serra

  Jocivania Pinheiro, Benjamin Bedregal, Regivan Santiago, and Helida Santos

  R. Vieira, L. Maciel, R. Ballini, and Fernando Gomide

  Renato Lopes Moura and Peter Sussner



   Renan Fonteles Albuquerque, Paulo D. L. de Oliveira, and Arthur P. de S. Braga

   José A. V. Florêncio, Madson L. D. Dias, Ajalmar R. da Rocha Neto, and Amauri H. de Souza Júnior

   Chryslayne M. Pereira, Moiseis S. Cecconello, and Rodney C. Bassanezi

  . . .

   Geraldo L. Diniz and Evanizio M. Menezes Jr. . . .

   Michael M. Diniz, Luciana T. Gomes, and Rodney C. Bassanezi

   Gino G. Maqui-Huamán, Geraldo Silva, and Ulcilea Leal

   Tiago Mendonça da Costa, Yurilev Chalco-Cano, Laécio Carvalho de Barros, and Geraldo Nunes Silva

   Marina Tuyako Mizukoshi

   Jônathas D. S. Oliveira, Luciana T. Gomes, and Rodney C. Bassanezi


  Magda S. Peixoto, Claudio M. Jonsson, Lourival C. Paraiba, Laécio C. Barros, and Weldon A. Lodwick

   Fabiola Roxana Villanueva and Valeriano Antunes de Oliveira

  XIV Contents



   Ralph Baker Kearfott and Dun Liu

   W. F. Mascarenhas

   Christian Servin and Vladik Kreinovich

   Juan Carlos Figueroa-García, Jhoan Sebastian Tenjo-García, and Camilo Alejandro Bustos-Tellez

   Francisco Zapata and Vladik Kreinovich

   Mona Abdolkarimzadeh, M. H. Fazel Zarandi, and O. Castillo


  Marylu L. Lagunes, Oscar Castillo, Fevrier Valdez, Jose Soria, and Patricia Melin

  Patricia Ochoa, Oscar Castillo, and José Soria

  Gustavo Pérez Hoyos


Formal Verification of a Fuzzy Rule-Based

Classifier Using the Prototype

Verification System

  ( ) B

  Solomon Gebreyohannes, Ali Karimoddini , Abdollah Homaifar, and Albert Esterline


Department of Electrical and Computer Engineering,

North Carolina A&T State University, 1601 East Market Street,

Greensboro, NC 27411, USA



Abstract. This paper presents the formal specification and verification

of a Type-1 (T1) Fuzzy Logic Rule-Based Classifier (FLRBC) using the

Prototype Verification System (PVS). A rule-based system models a

system as a set of rules, which are either collected from subject mat-

ter experts or extracted from data. Unlike many machine learning tech-

niques, rule-based systems provide an insight into the decision making

process. In this paper, we focus on a T1 FLRBC. We present the for-

mal definition and verification of the T1 FLRBC procedure using PVS.

This helps mathematically verify that the design intent is maintained

in its implementation. A highly expressive language such as PVS, which

is based on a strongly-typed higher-order logic, allows one to formally

describe and mathematically prove that there is no contradiction or false

assumption in the procedure. We show this by (1) providing the formal

definition of the T1 FLRBC in PVS and then (2) formally proving or

deducing rudimentary properties of the T1 FLRBC from the formal spec-


  Keywords: Fuzzy rule-based classifier Formal verification · Prototype verification system

1 Introduction

  Unlike many machine learning techniques, which treat systems as “black boxes” and model them using input-output relationships, Rule-Based Systems (RBSs) model systems using rules that can provide an insight into their decision mak- ing processes. RBCs are effective tools for encoding a human expert’s knowledge into an automated system

   ]. They can be used for different applications such as

  prediction and control applications. Recently, they have been used for a classifi- cation purpose. A fuzzy logic based RBS that is used for classification is called a Fuzzy Logic Rule-Based Classifier (FLRBC). It translates the expert’s heuristic knowledge into fuzzy “IF-THEN” statements (i.e., rules), which, along with an

2 S. Gebreyohannes et al.

  appropriate inference engine, are used for system classification

   ]. FLRBCs

  have been used to classify various kinds of items such as text, image

  , gesture


  and have been applied to several areas such as battlefield

  ground vehicles

   ], and so on. FLRBCs are also com-

  bined with genetic algorithms

  , decision trees, and other techniques to optimize their performance.

  Despite the great capabilities of FLRBCs and their wide range of applica- tions, a major challenge is that how can we be sure (i.e., mathematically ver- ify) that the design intent of an FLRBC is maintained in its implementation? Regarding to design procedures, FLRBC is no exception to the general point that natural language lacks the formality needed for requirement verification, i.e., with natural language, one cannot show (and hence cannot ensure) consis- tency and completeness of the procedure. Hence, a formal way (from a mathe- matical perspective) of representing the FLRBC design procedure and verifying its properties is necessary.

  Formal verification can be defined as a “systematic process based on math- ematical reasoning in order to verify that the design intent (specifications) is maintained during implementation”

  . There are different tools developed for

  conducting formal verification such as Prototype Verification System (PVS)



  . Using a higher-order theorem-proving system,

  such as PVS, it is possible to reach a much higher level of confidence compared to lighter formal methods. Formalizing specifications using PVS has been used in a range of applications. In

   ], a formal specification and verification of the

  requirements for an airline reservation system using PVS is presented. As a more sophisticated use of PVS in industrial applications,

  uses PVS for verification

  of two hardware examples, the pipelined microprocessor and n-bit ripple-carry adder. Butler

   ] also uses PVS for formal capturing of requirements of an

  autopilot (related to an early Boeing-737 autopilot). In

   ], PVS is used for

  analysis of a space shuttle software requirements. Despite the progress made on employing PVS for different verification applications, we are not aware of any PVS formalization of the FLRBC.

  This paper, therefore, proposes to use formal verification techniques to sys- temically and formally verify a fuzzy RBC whether it is consistent with required specifications. We present FLRBC specifications and verify them using the PVS framework. Our focus in this paper is on Type-1 (T1) FLRBC; however, the approach can be extended to other fuzzy RBCs. We encode PVS theories for the T1 FLRBC main components (viz., fuzzifier, inference, and defuzzifier), keeping their generality. This means that the developed PVS theories are not dependent on any particular application. Therefore, one can call and instantiate them to be used for any other application that utilizes the FLRBC technique.

  The rest of the paper is organized as follows. Section

   discusses a fuzzy logic

  rule-based classification. Section

   presents a brief introduction to PVS. Section


  presents the formal definition and verification of the T1 FLRBC. PVS theories are developed and formal proofs are shown. Section

   concludes this paper. Formal Verification of a Fuzzy Rule-Based Classifier Using PVS


  2 Fuzzy Logic Rule-Based Classification: An Informal Description

  In this section, we present fuzzy sets and systems preliminaries, and will discuss fuzzy rule-based classification, mainly borrowed from


  2.1 Fuzzy Sets and Systems Preliminaries This section introduces fuzzy sets and fuzzy logic systems. A fuzzy set is char- acterized by a membership function (MF), mapping the elements of a domain space or universe of discourse to the interval [0, 1]. A type-1 fuzzy set can be defined as follows: Definition 1.

  A type-1 fuzzy set A is a set function on universe X into [0, 1], i.e., µ A : X → [0, 1].

  A = {(x, µ A (x))|x ∈ X, 0 ≤ µ A (x) ≤ 1} (1) where the MF of A is denoted µ A (x) and is called a type-1 MF.

  A fuzzy system that operates on type-1 fuzzy sets (and crisp sets) is called a type-1 fuzzy system. Definition 2. A Type-1 fuzzy system contains four components - rules, fuzzifier, inference engine, and defuzzifier - that are interconnected as shown in Fig.



Once the rules have been established, the fuzzy system can be viewed as a mapping

  , . . . , x


from p inputs x = {x p } to an output y, and the mapping can be expressed

  1 quantitatively as y = f (x).

  Figure shows a type-1 fuzzy logic system. Note that x is a specific value of x. The components of this fuzzy system are described below.

  Fig. 1.

  A type-1 fuzzy system ] Rules are sets of IF-THEN statements that model the system and can have two commonly different structures.

4 S. Gebreyohannes et al.


  1. Zadeh’s l rule, l = 1, . . . , M , has a form: l l l l R , Z : IF x is F and . . . , x p is F THEN y is G (2)

  1 p l th l th


  where F is the i antecedent MF and G is the consequent MF of the l i rule. th

  2. Takagi, Sugeno, and Kang (TSK) l rule, l = 1, . . . , M , has a form: l l l l R , , . . . , x T SK : IF x is F and . . . , x p is F THEN y is g (x p ) (3)

  1 p

  1 l th l th

  1 where F is the i antecedent MF and g is the function for the l rule. i

  The fuzzifier maps a crisp input x = {x , . . . , x p } into a fuzzy set in X. There


  are two kinds of fuzzifiers: singleton and non-singleton. A singleton fuzzification ′ ′ l maps a specific value x into µ (x ) ∈ [0, 1] while a non-singleton maps into a i F i i type-1 fuzzy number.

  In the fuzzy inference engine, fuzzy logic principles are used to map fuzzy input sets in X ×. . .×X p , that flow through an IF-THEN rule (or a set of rules),

  1 into fuzzy output sets in Y . Each rule is interpreted as a fuzzy implication.

  A defuzzifier maps a fuzzy output of the inference engine to a crisp output y.

  2.2 Type-1 Fuzzy Logic Rule-Based Classifier This section discusses a singleton T1 FLRBC. The classifier consists of five components, as shown in Fig.

  by adding a comparator to a fuzzy system.

  The T1 FLRBC is used for a binary classification, i.e., it classifies its inputs as either Class 1 or Class 2.

  Fig. 2.

  A type-1 fuzzy logic rule-based classifier

  , x , . . . , x Recall that x is a set of p input features, i.e., x = {x

  1 2 p }, and y is an output of the fuzzy system.

  The rules of an FLRBC are a special case of Zadeh’s rule, in which the consequent is a singleton or TSK rule with a constant function

   ]. They are Formal Verification of a Fuzzy Rule-Based Classifier Using PVS th l


  characterized by MFs. For the l Mamdani rule l l l l ], l = 1, . . . , M, we have R : l l l A → G , which can be represented by µ (x, y), where A = F × . . . × F . A →G p

  1 For the consequent, a crisp value +1 is used for Class 1 and −1 is used for Class 2. l

  1 Class 1 y = (4)

  −1 Class 2 l Correspondingly, for the consequent sets, G , the MFs can be defined as l y l 1 = y

  µ G (y) = (5) l otherwise where y could be either +1 for Class 1 or −1 for Class 2. The fuzzy inference engine for this rule-base classifier is shown in Fig.


  Fig. 3. Fuzzy inference engine (, p. 108)

  The membership function of each fired rule for singleton input can be calcu- lated using a t-norm as: p l l l l T µ (x i ) = f (x), y = y i =1 F i µ B l (y) =

  (6) y l 0, = y where µ (x i ), i = 1, . . . , p, represents a singleton fuzzification and T is a t-norm F i operation.

  Using height defuzzification, the output can be calculated as: M l l f l =1 (x)y l y , y RBC (x) = = ±1 (7) M l f i (x)



  A classification then can be performed as: If y RBC (x) > 0, classify x as Class 1

  (8) If y RBC (x) ≤ 0, classify x as Class 2

3 Prototype Verification System


   ] is a formal verification environment which provides a highly expressive specification language based on a strongly-typed higher-order logic.

6 S. Gebreyohannes et al.

  The type system of PVS supports the use of both interpreted and uninter- preted types. Uninterpreted types support abstraction (using type) with a min- imum of assumptions on the type; e.g., var1: TYPE. Interpreted types, on the other hand, detail the type. For example, var2: TYPE = nat declaration intro- duces the type name var2 (interpreted) as a natural number. The PVS prelude


] provides definitions of a comprehensive collection of basic interpreted types,

  such as booleans, natural numbers, integers, reals, etc. The type system can be easily extended by defining new types using well-known type operators, such as functional, tuple and record combinators. It is also possible to define enumer- ated types and predicate subtypes as well as abstract data types, such as lists, stacks, binary trees, etc. Details on the PVS language may be found in the PVS

  Language Reference .

  Since the PVS language is so expressive, the type checking process is not decidable. For that reason, the type checker usually generates additional proof obligations, which must be proved by the user in order to verify the type con- sistency of the specification. Such obligations are called Type-Correctness Con- ditions (TCC).

  Formalizations in PVS are organized in theories, which include type, con- stant, variable, and formula definitions. Formulas can be constructed using propositional operators as well as first-order and higher-order quantifications. Every formula definition can be stated as an axiom or a theorem. Axioms are assumed to be valid in the specification, but formal proofs must be provided by the user for theorems. Specifications for many foundational and standard theories are preloaded into PVS as prelude theories.


Fig. 4. Theorem proving in PVS

  The theorem prover implemented in PVS is based on a formalism called


sequent calculus. A proof in such a calculus can be seen as a tree, where every

  node is a sequent composed of two collections of formulas, called antecedents and consequents, respectively. antecedents

  | (9) consequents The intuition behind the notion of a sequent is that it represents the logical consequence between the conjunction of its antecedents and the disjunction of its consequents. In order to prove that a formula, for example, α → β, is valid, the user must start with the sequent α ⊢ β (the operator ‘⊢’ denotes a sequent) Formal Verification of a Fuzzy Rule-Based Classifier Using PVS


  and try to reach trivially valid sequents by applying predefined proof rules to the leaves of the proof tree. Although proofs in PVS are constructed interactively, it does provide a considerable degree of automatization for a wide spectrum of cases. Details about proofs can be found in the PVS prover guide

  There are also PVS

  tutorials and applications developed in


  4 Formal Description and Verification of Fuzzy Rule-Based Classification

  This section presents the formal representation and verification of a singleton T1 FLRBC using PVS. Table

   summarizes the semantic mapping of T1 FLRBC components to PVS.


Table 1. Fuzzy RBC constructs to PVS mapping

Fuzzy RBC PVS Input feature Uninterpreted Type Output Enumerated type µ , consequent Subtypes Input, antecedent Finite sequence Membership function, rule Record type

Inference, defuzzification RECURSIVE function

  To formally describe a fuzzy RBC, we start by defining basic objects in the following sections.

  4.1 Basic TYPE Definition The first step in the design of a T1 FLRBC system is selecting features that act as antecedents. They are usually represented using their MFs. The antecedent features are encoded as PVS (uninterpreted) TYPEs and their MFs as RECORD TYPEs defined in the fbasic defs PVS theory. An MF consists of a range of values and a function that maps a value (within the range) into [0, 1]. fbasic_defs: THEORY BEGIN

  Feature: TYPE m: TYPE = nat mu: TYPE = {u:real| 0 <= u AND u <= 1} MF: TYPE = [# range: [real,real],

  f: [{x:real|range‘1<=x

8 S. Gebreyohannes et al.

  AND x<=range‘2}-> mu] #] Feat_MF: TYPE = [# feat: Feature, mf: MF #]

  . . . A rule (R) maps a set of antecedents (A = F × F × F p ) to a consequent


  2 (G), i.e., R : A → G; it is usually described by the MF µ R (X, y) = µ A →G (X, y).

  In an RBC, the consequent is a singleton where ‘Class 1’ is represented by +1 and ‘Class 2’ is represented by −1. Hence, a consequence is represented as a subtype in PVS. Each rule has a specifier l <= M , where M is the total number

  , x , x , . . . , x of rules. An input X has p features, x p . We represent the input





  as a finite sequence of real values. A fuzzy rule is encoded using PVS RECORD with accessors rule number, antecedents, and consequents.

  Input: TYPE = finseq[real] Output: TYPE = {class1, class2} Antecedents: TYPE = finseq[Feat_MF] Consequent: TYPE = {n: nat| n = -1 OR n = 1} Rule: TYPE = [# rulen: l, antcs: Antecedents, consq: Consequent #]

  Rules: TYPE = finseq[Rule] END fbasic_defs

  4.2 Describing Fuzzy RBC Using PVS This section formally defines the T1 FLRBC using PVS. We put the definition in a fls theory. We start by importing fbasic defs theory defined in Sect.


  and declaring input and rule variables. Let X,r, and rs be input, a rule, and a set of rules, respectively. There are p inputs and M rules. fls: THEORY BEGIN

  IMPORTING fbasic_defs p,M: VAR nat X:

  VAR Input r:

  VAR Rule rs:

  VAR Rules The number of antecedents in a rule r should be the same as the length of the input vector x. This is encoded as a PVS AXIOM. inpt_length: AXIOM FORALL (X,r): X‘length = r‘antcs‘length Now, we can represent the inference engine. Based on Eq.

  the inference engine

  can be represented as a PVS RECURSIVE function as follows:

  Formal Verification of a Fuzzy Rule-Based Classifier Using PVS


  fl(X,r,p): RECURSIVE mu =

  IF p=0 THEN 0 ELSE r‘antcs‘seq(p)‘mf‘f(X‘seq(p))*fl(X,r,p-1) ENDIF

  Measure p The defuzzified output of the RBC can be obtained using Eq.

   is encoded using a PVS RECURSIVE function as:

  sumfy(X,rs,M): RECURSIVE mu =

  IF M=0 THEN 0 ELSE fl(X,rs‘seq(M),X‘length)*rs‘seq(M)‘consq + sumfy(X,rs,M-1) ENDIF

  Measure M Similarly, the denominator of Eq.

   is encoded using a PVS as:

  sumf(X,rs,M): RECURSIVE mu =

  IF M=0 THEN 0 ELSE fl(X,rs‘seq(M),X‘length) + sumf(X,rs,M-1) ENDIF

  Measure M Therefore, Eq.

   can now be represented using PVS as:

  yRBC(X,rs): real = sumfy(X,rs,rs‘length)/sumf(X,rs,rs‘length) The final decision of the classifier is based on the sign of the defuzzified output as shown in Eq.

  This is encoded using a PVS IF-ELSE statement.

  decision(X,rs): Output =

  IF (yRBC(X,rs) > 0) THEN class 1 ELSE class 2 ENDIF

  END fls This completes the formal definition of the T1 FLRBC process.

  4.3 Formal Verification This section presents the formal verification - well-formedness (to verify our spec- ification is correct, i.e., free of contradiction or false assumption) and requirement verification (to verify a given requirement or property can be deduced from the specification).

10 S. Gebreyohannes et al.

  Well-Formedness. PVS requires theorem proving in order to guarantee that the specification is type correct

   ]. Type checking of the fbasic defs theory

  generates no TCCs, but the fls theory does generate TCCs. For example, one TCC generated is yRBC_TCC1: OBLIGATION

  FORALL (X:Input, rs: Rules): sumf(X, rs, rs‘length) /= 0 The denominator of Eq.

   needs to be non-zero to avoid division by zero. This was not contained in our definition. Therefore, we add a PVS AXIOM for this.

  nz_tnorm: AXIOM FORALL (X,rs): NOT (sumf(X,rs,rs‘length) = 0) Any TCC should be discharged if we are to guarantee the correctness (no con- tradiction nor false assumptions) of the specification. We revise the theory for no TCC. We successfully discharged some of the TCCs using the axioms. The rest of the TCCs are proved automatically by a PVS standard strategy (tcc).

  Once all the TCCs are discharged, the theories are well-formed; i.e., there is no contradiction (nor false assumption) in the declaration. However, we do not know yet whether it satisfies any given properties (or requirements). We show this in Sect.


  Verification of Properties.

  Now, since the T1 FLRBC is formally represented, one can mathematically verify properties of the T1 FLRBC by encoding them as PVS THEOREMs and proving them interactively using appropriate prover commands. In this section, we prove some rudimentary properties.

  Lemma 1.

  The output of the T1 FLRBC should be either Class 1 or Class 2.

We define two PVS predicates: first we check independently that if a given output

is Class 1 or Class 2.

  class1?(X,rs): bool = decision(X,rs) = class1 class2?(X,rs): bool = decision(X,rs) = class2

  Then, the theorem will be the XOR of the two predicates.

  bin_class: THEOREM FORALL(X,rs):xor(class1?(X,rs),class2?(X,rs)) This theorem is discharged automatically by a prover command grind. Q.E.D.

  Let us check the dependence of the output of the classifier on the rules. Lemma 2.

  (a) If the output of the classifier is Class 1, then there exists a rule with a consequent of 1.

  class1_det: THEOREM decision(X,rs) = class1 IMPLIES EXISTS r:member(rs,r) AND r‘consq = 1


(b) If the output of the classifier is Class 2, then there exists a rule with a

consequent of 1.

  Formal Verification of a Fuzzy Rule-Based Classifier Using PVS


  class2_det: THEOREM decision(X,rs) = class2 IMPLIES EXISTS r:member(rs,r) AND r‘consq = -1 This is proved again using the prover command grind. Q.E.D.

  Finally, let us examine the defuzzified output of the fuzzy system from Eq.


  Lemma 3.

  The numerator in Eq. l is less than or equal to the denominator. The

two values are equal only if y = 1, for l = 1, . . . , M . Therefore, the following

theorem needs to be proved from our formal definition.

  yrbc_def: THEOREM FORALL (X,rs): sumfy(X,rs,rs‘length) <= sumf(X,rs,rs‘length) It too is proved using the prover command grind. Q.E.D.

  Similarly, more properties can be formally represented and then deduced from the theories. Also, if a particular example uses the FLRBC technique (and so its formalization will be represented by instantiating the PVS theories developed in this paper), then application-dependent properties can be proved.

5 Conclusion

  This paper presented the formalization of a T1 FLRBC using PVS. We devel- oped PVS theories to formally represent the FLRBC procedure. We also verified some rudimentary properties of the FLRBC by discharging PVS type-checking requirements and proving theorems. For the future, we aim to formalize a specific application that utilizes the FLRBC technique by instantiating the developed PVS theories.


  This research is supported by Air Force Research Laboratory and

Office of the Secretary of Defense under agreement number FA8750-15-2-0116 as well

as US ARMY Research Office under agreement number W911NF-16-1-0489.


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