REAL-VALUED GENETIC ALGORITHMS FOR FUZZY GREY PREDICTION SYSTEM

Authors
Citation
Yp. Huang et Ch. Huang, REAL-VALUED GENETIC ALGORITHMS FOR FUZZY GREY PREDICTION SYSTEM, Fuzzy sets and systems, 87(3), 1997, pp. 265-276
Citations number
12
Categorie Soggetti
Computer Sciences, Special Topics","System Science",Mathematics,"Statistic & Probability",Mathematics,"Computer Science Theory & Methods
Journal title
ISSN journal
01650114
Volume
87
Issue
3
Year of publication
1997
Pages
265 - 276
Database
ISI
SICI code
0165-0114(1997)87:3<265:RGAFFG>2.0.ZU;2-J
Abstract
A genetic-based fuzzy grey prediction model is proposed in this paper. Instead of working on the conventional bit by bit operation, both the crossover and mutation operators are real-valued handled by the prese nted algorithms. To prevent the system from turning into a premature p roblem, we select the elitists from two groups of possible solutions t o reproduce the new populations. To verify the effectiveness of the pr oposed genetic algorithms, two simple functions are first tested. The results show that our method outperforms the conventional one no matte r whether from the viewpoint of the number of iterations required to f ind the optimum solutions or from the final solutions obtained. The re al-valued genetic algorithms are then exploited to optimize the fuzzy controller which is designed to perform the compensation job. Two diff erent types of fuzzy inference rules are considered to compensate for the predicted errors from the grey model. The difficulty encountered i n applying the genetic algorithms to adjusting the fuzzy parameters is also discussed. Based on the simulation results from the problems of the weather forecast, we found that the proposed methodology is very e ffective in determining the quantity of compensation for the predicted outputs from the traditional grey approach. (C) 1997 Elsevier Science B.V.