X 2 . Again, how well did this work? Plot your
b 2 X 2 . Again, how well did this work? Plot your
results. Is anything wrong? Calculate a sum of the
squares error and R 2 .
d. Break up the problem into three sections (look at
the plot) and solve it with three linear regression
61.9 models, one for each section. How well did this
work? Plot your results. Calculate a sum of the
squares error and R . Is this modeling approach
appropriate? Why or why not?
e. Build a neural network to solve the original prob- lem. (You may have to scale the X and Y values to
. C H A P T E R 1 2 A D V A N C E D INTELLIGENT SYSTEMS
each of the original data items). H o w well did this or even survey the class for weight, gender, and work? Plot your results. Calculate a sum of the
height and try to predict height based on the other
two. (Hint: U s e U.S. Census data, on this book's Web f. Which method worked best and why?
squares error and R 2 .
site or at www.census.gov, by state to identify a rela- 3. Build a real-world neural network. Using demo soft-
tionship between education level and income.) H o w
ware downloaded from the Web (Braincel at
good are your predictions? Compare the results to www.promland.com or another), identify real-world
predictions generated by standard statistical methods
data (e.g., start searching on the Web at
(regression). Which method was better? H o w could www.research.ed.asu.edu or use data from an organi-
your system be embedded in a DSS for real decision zation with which someone in your group has a con-
making?
tact) and build a neural network to make predictions. 4. Fuzzy logic. Survey your class by having everyone Topics might include sales forecasts, predicting suc-
write down a height representing tall, medium, and cess in an academic program (predict GPA from high
short for men and for women. Tally the results and school rating and SAT scores—see ftp://psych
determine what is meant by tall, medium, and short .colorado.edu/pub/stat/gpa.txt, being careful to look
in a fuzzy way. Create the membership functions in out for "bad" data, e.g., GPAs of 0.0), housing prices,
these sets and examine the results.
• INTERNET EXERCISES 1. Case-based reasoning has been used lately for data
5. Examine fuzzy logic vendor Web sites and identify mining. Explore the Web to find vendors and
the kinds of problems to which fuzzy logic is cur- research literature about this topic,
rently being applied. Find a demo version of a 2. Explore the Web sites of several neural network
system and try it out. Report your findings to the vendors, such as California Scientific Software
class.
(www.calsci.com), NeuralWare Inc. (www.neuralware 6. U s e the Internet to find information about neuro- .com), and Ward Systems Group (www.wardsystems
fuzzy logic systems.
.com), and review some of their products. Download 7. Access the Web and e-journal in your library to at least two demos and install, run, and compare
find at least three reports on the use of integrated them.
methods for intelligent decision support. Evaluate 3. Explore the Web to identify the current status of
whether the applications are feasible in the real neural network research.
world.
4. Examine genetic algorithm vendor Web sites and investigate their business applications. What kinds of applications are most prevalent?
Parts
» Decision Support System And Intelligent System 7th Edition Turban Aronson Liang 2005
» DECISION -MAKING: THE INTELLIGENCE PHASE
» DECISION-MAKING: THE CHOICE PHASE
» SUPPORT FOR THE I N T E L L I G E N C E P H A S E
» OPENING VIGNETTE: DUPONT SIMULATES RAIL TRANSPORTATION SYSTEM AND AVOIDS COSTLY CAPITAL EXPENSE 1
» CERTAINTY, UNCERTAINTY, AND RISK 3
» MSS MODELING WITH SPREADSHEETS
» THE NATURE AND SOURCES OF DATA
» THE WEB/INTERNET AND COMMERCIAL DATABASE SERVICES
» DATABASE MANAGEMENT SYSTEMS IN DECISION SUPPORT SYSTEMS/BUSINESS INTELLIGENCE
» BUSINESS INTELLIGENCE/BUSINESS ANALYTICS
» Repeat steps 3 to 5 until you have reached the pre- specified maximum number of clusters.
» INTRODUCTION TO DSS DEVELOPMENT
» THE TRADITIONAL SYSTEM DEVELOPMENT LIFE CYCLE
» Obtaining executive-level buy-in. • Correctly estimate how difficult or complex a pro-
» PROTOTYPING: THE DSS DEVELOPMENT METHODOLOGY
» N A C T I O N 6.19 USER INVOLVEMENT IS CRITICAL IN SYSTEMS IMPLEMENTATION
» The four cells are organized along the two dimensions of time and place.
» Make money (sell your courseware)
» CREATIVITY AND IDEA GENERATION CREATIVITY
» EXECUTIVES' ROLES AND INFORMATION NEEDS
» SOFT INFORMATION IN ENTERPRISE SYSTEMS
» SUPPLY AND VALUE CHAINS AND DECISION SUPPORT
» SUPPLY CHAIN PROBLEMS AND SOLUTIONS INTRODUCTION
» CUSTOMER RELATIONSHIP (RESOURCE) MANAGEMENT (CRM) SYSTEMS
» BEST THINGS YOU CAN DO WITH YOUR DATA
» FRONTLINE DECISION SUPPORT SYSTEMS
» THE FUTURE OF EXECUTIVE AND ENTERPRISE INFORMATION SYSTEMS 2
» APPROACHES TO KNOWLEDGE MANAGEMENT THE PROCESS APPROACH
» EVOLUTION OF ARTIFICIAL INTELLIGENCE
» THE ARTIFICIAL INTELLIGENCE FIELD
» PROBLEM AREAS SUITABLE FOR EXPERT SYSTEMS
» KNOWLEDGE ACQUISITION FROM MULTIPLE EXPERTS
» KNOWLEDGE VERIFICATION AND VALIDATION
» S I N A C T I O N 11.9 RULE-BASED SYSTEMS TACKLE EMPLOYEE SHRINK
» Search the Internet to find a knowledge-acquisition development of Web-based expert systems. How
» BASIC CONCEPT OF NEURAL COMPUTING
» DEVELOPING NEURAL NETWORK-BASED SYSTEMS
» DEVELOPING GENETIC ALGORITHM APPLICATIONS
» DEVELOPING INTEGRATED ADVANCED SYSTEMS
» X 2 . Again, how well did this work? Plot your
» OPENING VIGNETTE: SPARTAN USES INTELLIGENT SYSTEMS TO FIND THE RIGHT PERSON AND REDUCE TURNOVER
» WEB-BASED INTELLIGENT SYSTEMS
» INTELLIGENT AGENTS: AN OVERVIEW
» M-COMMERCE, L-COMMERCE, AND PERVASIVE COMPUTING
» OPENING VIGNETTE: ELITE CARE SUPPORTED BY INTELLIGENT SYSTEMS
» THE IMPACTS OF MSS: AN OVERVIEW
» DECISION-MAKING AND THE MANAGER'S JOB
» ISSUES OF LEGALITY, PRIVACY, AND ETHICS
» INTELLIGENT SYSTEMS AND EMPLOYMENT LEVELS
» Rosati. (2001, September). "Data Integration in Celik, M. (2001, July). "Catching Up with Expert
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