Proceeding of 2013 International Seminar on Climate Change and Food Security ISCCFS 2013
Palembang, South Sumatra -Indonesia, 24-25 October,2013
119 The characteristic of GA is different from other optimization techniques. The differences are found in
the operating system which GA runs based on the coding set in the parameter, the selecting system is in a population, information of objective function as technique for evaluating the individual who has the best
solution, not the derivative of a function. In addition, AG uses probabilistic transition rules, not deterministic rules.
The variables used in the genetic algorithm are as follows: 1 the fitness function, which is owned by each individual for analyzing the individuals level of compliance with the criteria to be achieved. Fitness is
maximized with the application of genetic algorithms, 2 population is the number of individuals, who are involved in every generation, 3 Opportunity probability in recombination occurs in a generation, 4
opportunity mutations occur in every transfer bits, and 5 number of generations to be established would determine the number of the application of genetic algorithms.
Logical fuzzy and PID techniques have been widely applied in the control system. The steps of the application are as follows: 1 calculation of the error and error difference, 2 fuzzification, 3 determination
of the control rules decision matrix and calculation of the maximum value, and 4 de-fuzzification. Proportional integral differential control and its parameters follow Ziegler and Nichols method.
4. Determination of Optimal Set Point and Controlling
Optimal set point in this system is the environmental conditions which are based on the maximum of plant products harvested. The optimal set points in biological-environment adaptive control system are the
optimal environmental conditions for the desired plant produce to be harvested. It could be in quality, quantity, or the preferences of consumers, or the combination between those factors. The environmental
conditions are not always suitable for normal plant growth, but they might inhibit the growth of particular part of plant which is intended to increase the quality of the particular part of the plant produce. For
examples, water stressed treatment can increase the crispness of vegetables, wherein in such water stressed treatment, the environment is controlled for inhibiting the growth of plant.
The environment conditions can also affect the taste of the pineapple, the smell of tobacco, and others. Plant also has the adaptability to changing environmental conditions, so the optimal environmental
conditions may vary, depending on the objectives of manipulated treatment itself. In order to obtain the specific criteria of the plant produce, it is necessary to provide facilities for the determination of the set point
to anticipate the requirements. The determination of the facility is equipped with a sub-optimal plant model systems and image sensors. The biological-environment adaptive control system uses two operators for
identification and optimization. The operators are the artificial neural network and genetic algorithms.
Environmental parameters are associated with minimum error and stable control performance. The determination of the optimal parameters control facility is integrated with the simulation system. The
optimal control parameters are determined through simulations by using ANN operator or heat transfer models. The relationship between the internal and external environment of the plant house biological
environment is explained by the model environment. Then, the optimal control parameters are determined by using genetic algorithm operator and equipped with the control menu options.
5. Conclusions
