Design of a neural-fuzzy controller based on fuzzy differential competitive learning

In this paper, a novel neural-fuzzy controller based on fuzzy differential competitive learning is proposed. Since one of the most important parts is the generation of the fuzzy rules in the design of the fuzzy control system, a fast learning algorithm, fuzzy differential competitive learning (FDCL), for the generation of the rules is applied in the fuzzy control system. The FDCL algorithm adopts a principle of learn according to how well it wins. Unlike the previous competitive learning algorithm such as crisp competitive learning algorithms where only one neuron will win and learn at each competition step every neuron in the neural network based on FDCL algorithm will along with its different distance to the input pattern and learns the pattern accordingly. Compared with the ordinary competitive learning algorithm the proposed FDCL algorithm has the various distinguishing features. The FDCL algorithm is implanted in the neural network based fuzzy system and the network adopted is fuzzy associative memory system (FAMS) which simulates the knowledge representation and inference process by using fuzzy notation and by association in neural networks. In FAMS the fuzzy rules will be generated by clustering the input-output training data through the FDCL paradigm. By using the FDCL algorithm the neural network can highly refine knowledge and represent the expert experience.