30 Neural Network Case Studies with MATLAB Implementation

In this documentation, I would like to share with you a series of MATLAB-based neural network case analyses. The collection comprises 30 practical examples, each accompanied by complete source code implementations. These case studies demonstrate various neural network architectures and training methodologies, including multilayer perceptrons, radial basis function networks, and self-organizing maps. The code examples incorporate essential techniques such as data normalization, weight initialization strategies, and performance evaluation metrics. Each implementation provides hands-on experience with MATLAB's neural network toolbox functions like 'feedforwardnet', 'patternnet', and training algorithms such as 'trainlm' (Levenberg-Marquardt) and 'trainscg' (scaled conjugate gradient). These practical examples will significantly enhance your understanding and application skills in neural network technology for solving real-world pattern recognition, prediction, and classification problems.