MATLAB Code Implementation for Neural Networks
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Neural networks represent a computational model designed to simulate neural mechanisms and construct empirical models for solving diverse problems across various application scenarios. Inspired by biological neurons, this model mimics the connection patterns and information transmission processes within the brain to achieve learning and decision-making capabilities. Through neural networks, we can significantly enhance data processing and pattern recognition abilities, making them fundamental to machine learning, artificial intelligence, and related fields. In MATLAB implementation, key functions like feedforwardnet create feedforward architectures, while training algorithms such as Levenberg-Marquardt (trainlm) optimize weights through backpropagation. The typical workflow involves data normalization using mapminmax, network configuration with hidden layers, and performance validation through confusion matrices (confusionmat).
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