基于双重遗传算法的动态模糊系统设计与优化  

Design and optimization of dynamic fuzzy system based on double-layer genetic algorithms

作　　者：杨孔雨 刘洪杰 王秀峰 

机构地区：[1]南开大学信息技术科学学院

出　　处：《系统工程与电子技术》2005年第6期1025-1029,共5页Systems Engineering and Electronics

基　　金：山东省教育厅科技计划项目基金资助课题(J04A12)

摘　　要：设计了一种动态模糊系统模型,该模型能够动态地进行规则自学习,从而减少了规则学习的计算量。提出优化该模型的双重遗传算法:外层采用整数编码,用来训练系统的结构,内层采用实数编码,用来训练系统的参数;内层GA得到的最佳染色体适应值用来评价外层GA相应染色体。该模型结构简单,便于实现,并可离线优化,在线预测。通过应用于实际股市行情的预测和效率分析,不论从预测的结果还是从运行效率看,都收到了满意的效果。A dynamic fuzzy system forecast model is proposed. The mode can dynamically learn rules by if self and reduce the computational load of rules learning. Then a double-layer genetic algorithm which optimizes the model is proposed. In the algorithm, the external-layer adopts integer codes to train the structure of system and the internal-layer adopts real codes to train the parameters of system. The fitness value of the optimal chromosome from internal-lays GA is used to evaluate corresponding chromosomes of external-layer GA. This model has a simple structure and is easy to implement, and it can be optimized off-line and forecast on-line. Through its application to actual stock market forecast and efficiency analysis, the model is satisfactory both in forecast results and in work efficiency.

关 键 词：动态模糊系统 设计与优化 遗传算法 预测模型 

分 类 号：TP13[自动化与计算机技术—控制理论与控制工程]