Explore MATLAB source code curated for "权值优化" with clean implementations, documentation, and examples.
This article provides a comprehensive introduction to Particle Swarm Optimization (PSO) algorithm along with MATLAB source code implementations. PSO represents a cutting-edge optimization technique for smart antenna weight configuration, featuring swarm intelligence principles inspired by natural collective behaviors like bird flocking or fish schooling.
The weight optimization of BP neural networks represents an unconstrained optimization problem with real-number encoding, making it suitable for direct implementation using MATLAB's genetic algorithm toolbox. The provided code demonstrates nonlinear regression for a system with 19 input variables and 1 output variable. For adaptation to different scenarios, users need only modify the encoding/decoding functions. The implementation requires the GAOT (Genetic Algorithm Optimization Toolbox) with enhancements including optimization algorithm selection, automated parameter tuning, and parallel computation capabilities.
Utilizing the advanced Imperialist Competitive Algorithm (ICA) to optimize initial weights and thresholds of BP neural networks for wind power prediction, featuring comprehensive datasets and practical examples with ICA serving as the main program.
A hybrid intelligent optimization approach combining evolutionary computation with traditional neural network training for enhanced performance and robustness
Developed BP Algorithm Enhanced with Genetic Algorithm
A genetic algorithm-based toolbox for optimizing neural network weights with enhanced code implementation features
Genetic Algorithm (GA) Training of BP Network Weights and Thresholds with Implementation Strategy
Implementation of a standard Elman neural network combined with Particle Swarm Optimization (PSO) for improved performance