Neural Network Theory and Applications with MATLAB Toolbox

This article provides an in-depth exploration of neural network theory and practical applications using MATLAB's Neural Network Toolbox. We will systematically examine various application scenarios supported by the toolbox, accompanied by the original source code from the book for reader reference. The discussion covers fundamental neural network concepts including neuron structures, network architectures (such as multilayer perceptrons and convolutional networks), and their implementation through MATLAB functions like feedforwardnet and patternnet. The article delves into training algorithms (e.g., backpropagation with trainlm Levenberg-Marquardt optimization), activation functions, and data preprocessing techniques using MATLAB's mapminmax function. We analyze both the advantages and limitations of neural networks in real-world applications, demonstrating practical implementations through code examples showing dataset partitioning (dividerand), network configuration, and performance validation (confusionmat). Through this comprehensive guide, readers will gain robust theoretical understanding and practical skills to implement neural network solutions for complex engineering problems using MATLAB's computational framework.