Recursive T-S Fuzzy Neural Network Learning Algorithm
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The recursive T-S fuzzy neural network learning algorithm is a training approach that utilizes recursive methods to optimize T-S fuzzy neural networks. The core concept of this algorithm involves employing genetic algorithms to fine-tune network parameters. This recursive learning algorithm demonstrates superior adaptability to diverse input data while enhancing overall network performance. By implementing genetic algorithms for parameter optimization, the method effectively explores and exploits the network's potential capabilities, resulting in improved learning outcomes. Key implementation aspects include chromosome encoding of network parameters (membership function centers, widths, and consequent parameters), fitness evaluation based on prediction accuracy, and recursive weight updates through crossover and mutation operations.
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