Design and optimization of dynamic fuzzy system based on double-layer genetic algorithms
Kongyu Yang, Hongjie Liu · Systems engineering and electronics · 2005
A dynamic fuzzy system forecast model is proposed. The mode can dynamically learn rules by ifself 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 interna-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.