Perceptron Neural Network, Linear Network, Backpropagation Neural Network, and Radial Basis Function Network Implementation

This implementation provides MATLAB-based solutions for multiple neural network models, including perceptron neural networks, linear networks, backpropagation (BP) neural networks, and radial basis function (RBF) networks. These models are applicable across various domains such as pattern recognition, predictive analytics, and optimization problems. The perceptron neural network implementation utilizes simple threshold-based activation functions for binary classification tasks, with weight updates calculated through error correction learning rules. The linear network employs purelin transfer functions for linear approximation problems, using least mean square (LMS) algorithms for weight adaptation. The backpropagation neural network features multi-layer architecture with sigmoid activation functions, implementing gradient descent optimization with momentum factors to accelerate convergence and avoid local minima. The radial basis function network uses Gaussian basis functions for hidden layer transformations, with center selection algorithms and width parameter optimization for function approximation. All implementations include data preprocessing routines, network initialization methods, training algorithms with convergence criteria, and performance evaluation metrics. The code structure follows modular design principles, allowing easy customization of network parameters, layer configurations, and training specifications for different application scenarios.