Professional MATLAB source code with comprehensive documentation and examples
A simple yet practical simulation framework for second-order multi-agent system consensus, suitable for beginners and university students. This model can be modified for experiments and includes code implementation details for velocity and position c
Urban traffic flow exhibits high complexity, time-varying characteristics, and randomness. Real-time accurate traffic flow prediction is crucial for intelligent transportation systems, particularly in advanced traffic management and traveler informat
A practical BP neural network prediction implementation featuring comprehensive code comments and high adaptability - easily modifiable for various applications, ideal for beginners learning neural networks.
This method implements handwriting recognition using a Backpropagation (BP) neural network with accelerated training techniques. It enables rapid pattern recognition for a class of related handwritten fonts with high accuracy rates. When font variati
Essential learning path and practical implementation tips for AdaBoost machine learning algorithm beginners
Comprehensive guide to optimizing SVM parameters C and G using three methods: Grid Search, Genetic Algorithm, and Particle Swarm Optimization, complete with algorithm explanations and implementation insights for practical learning.
A comprehensive BP neural network implementation with excellent performance characteristics, suitable for various engineering applications including financial modeling, medical diagnostics, and traffic prediction systems.
Standard Particle Swarm Optimization (PSO) tends to converge prematurely, making it challenging to find the global optimum, necessitating algorithmic improvements.
This program implements multiclass classification on the IRIS dataset, featuring detailed training procedures and prediction accuracy evaluation. The implementation includes comprehensive classification modeling and achieves high precision, successfu
Development Background Tabu Search (TS), first proposed by Glover in 1986, extends local neighborhood search as a global stepwise optimization algorithm that simulates human intellectual processes. Key Technology The TS algorithm employs a flexible m
A well-optimized Meanshift clustering algorithm implementation by an international developer, complete with runnable examples and practical demonstrations.
This package provides a MATLAB implementation of the renowned AdaBoost algorithm, featuring enhanced threshold optimization to achieve superior classification performance through improved weak classifier selection and weighting mechanisms.
This quantum particle swarm optimization algorithm is implemented using MATLAB programming, enabling rapid optimization of multi-dimensional functions with reduced susceptibility to local optima convergence.
A practical MATLAB-based PSO source code implementation with comprehensive documentation and parameter optimization guidance
A MATLAB-based implementation example of RBF neural network modeling, featuring practical code demonstrations and algorithm explanations to facilitate effective learning of RBF modeling techniques
This program implements Support Vector Machine (SVM) regression algorithms, providing practical solutions with comprehensive code documentation for machine learning applications.
Implementing genetic algorithm optimization for simple functions using MATLAB, serving as a practical programming example with code implementation details.
A comprehensive guide to RBF neural network algorithms, providing substantial assistance and guidance for learners studying RBF networks with practical code implementation details.
MATLAB implementation of Multi-Objective Artificial Bee Colony algorithm - ready-to-run code with optimization capabilities for multiple objective functions
The Artificial Fish Swarm Algorithm (AFSA) is one of the most effective swarm intelligence optimization algorithms, inspired by the collective movement and social behaviors of fish. This algorithm simulates a series of instinctive behaviors where fis