Explore MATLAB source code curated for "人工神经网络" with clean implementations, documentation, and examples.
Hybrid Intelligent Algorithm: Utilizing Artificial Neural Networks to approximate uncertain functions and constraints, combined with Particle Swarm Optimization to solve stochastic programming models for IEEE 33-node systems with multiple wind power integration points.
Using artificial neural networks for PID parameter tuning represents the current trend in PID control and artificial intelligence algorithms, enabling adaptive optimization through machine learning techniques.
Implementation of the error learning algorithm for single-layer perceptrons in artificial neural networks with code-level insights
This project implements speech recognition by combining LPC (Linear Predictive Coding) parameters extracted from audio signals with artificial neural networks for classification. The implementation demonstrates feature extraction techniques and neural network architecture suitable for speech pattern recognition.
Research on Algorithm for Applying CMAC Network in Artificial Neural Networks to Robotic Arm Control - Featuring Implementation Approaches and Key Function Descriptions
MATLAB programming for artificial neural networks with BP network example focusing on inbound tourist number prediction, which provides valuable references for tourism management and government departments. Establishing a scientifically sound three-layer BP neural network model with input layer, hidden layer, and output layer is crucial for ensuring accurate and reliable forecasting results.
Implementation of common digital signal modulation recognition using artificial neural networks with recognition rate analysis and optimization strategies
Source code implementation using Particle Swarm Optimization (PSO) to train artificial neural networks, featuring weight optimization and fitness function evaluation.
A ready-to-use MATLAB-based artificial neural network program featuring intuitive implementation, easy-to-understand code structure, and immediate functionality for various machine learning applications.
Implementing edge detection with artificial neural networks yields excellent experimental results, demonstrating robust performance in identifying image boundaries through optimized network architectures.