MATLAB Implementation of T-S Fuzzy Neural Network for Endpoint Detection

This article explores the implementation of T-S fuzzy neural networks for endpoint detection applications. The T-S fuzzy neural network represents a neural network architecture based on fuzzy logic theory, commonly applied in pattern recognition, control systems, and signal processing domains. A critical aspect of employing T-S fuzzy neural networks for endpoint detection involves developing corresponding MATLAB programs. We provide a comprehensive MATLAB implementation featuring key components such as fuzzy rule generation using Gaussian membership functions, neural network training through backpropagation algorithms, and endpoint classification logic. The MATLAB code includes functions for data preprocessing, fuzzy inference system creation using the ANFIS framework, and performance evaluation metrics. We also examine the advantages of T-S fuzzy neural networks in endpoint detection scenarios, comparing them with traditional methods like energy-based detection and statistical approaches. The implementation demonstrates how the network's adaptive learning capability and fuzzy reasoning mechanism contribute to robust endpoint detection in varying noise conditions. Comparative analysis highlights why T-S fuzzy neural networks serve as powerful tools for complex signal processing tasks, particularly when dealing with nonlinear and uncertain systems.