Neurofuzzy learning of strategies for optimal control problems

Various techniques have been proposed to automate the weight selection process in optimal control problems; yet these techniques do not provide symbolic rules that can be reused. We propose a layered approach for weight selection process in which Q-learning is used for selecting weighting matrices and hybrid genetic algorithm is used for selecting optimal design variables. Our approach can solve problems that genetic algorithm alone cannot solve. More importantly, the Q-learning's optimal policy enables the training of neuro-fuzzy systems which yields reusable knowledge in the form of fuzzy if then rules. Experimental results show that the proposed method can automate the weight selection process and the fuzzy if-then rules acquired by training a neuro-fuzzy system can solve similar weight selection problems.