MATLAB Code Implementation: Neural Network Example Collection

This document provides a collection of neural network implementation examples in MATLAB to demonstrate their application scope. Below is an overview of these examples: 1. Single-layer Linear Neural Network Example: Demonstrates how to implement a single-layer linear neural network using MATLAB's neural network toolbox to solve simple classification problems. The implementation includes weight initialization, forward propagation, and basic training algorithms. 2. Perceptron Neuron for Complex Input Vector Classification: Introduces how perceptron neurons can handle classification problems with multiple feature inputs. The code showcases feature preprocessing, perceptron training rules, and decision boundary visualization for multi-dimensional data. 3. Perceptron-based Neural Network for Complex Classification Problems: Demonstrates advanced perceptron neural network implementations for handling high-dimensional feature vectors. Includes code for multi-layer perceptron structures, activation functions, and performance evaluation metrics. 4. Numerical Analysis Program with MATLAB-GUI: Provides a MATLAB GUI-based numerical analysis program example that integrates neural networks for solving computational problems. The implementation features interactive user interfaces, data visualization components, and real-time result display. 5. Backpropagation Network for Function Approximation Source Code: Presents complete source code for using BP networks to approximate mathematical functions. The implementation covers network architecture design, gradient descent algorithms, error calculation, and convergence testing with detailed comments. 6. Self-organizing Feature Map Application Example: Demonstrates practical applications of self-organizing maps (SOM) using MATLAB's neural network toolkit. Includes code for topology preservation, neighborhood functions, and clustering visualization for real-world datasets. Through these comprehensive examples, readers can gain practical understanding of neural network applications and adapt these implementations to their specific problem domains. Each example includes thorough code documentation and algorithm explanations to facilitate learning and customization.