Applying adaptive structured genetic algorithm to reasoning and learning method for fuzzy rules using neural networks

In this paper, we present a reasoning and learning method for fuzzy rules using neural networks with an adaptive structured genetic algorithm. This adaptive structured genetic algorithm is to determine the neural network structures and their input weights by an evolutionary process. Without using general learning algorithm in neural networks, the adaptive structured genetic algorithm can generate or annihilate the specified units respectively in hidden layer to achieve an overall good system.