RBF Neural Network Simulation (Primarily for Function Fitting and Pattern Classification)
MATLAB Example Implementation of RBF Neural Network for Function Approximation and Pattern Classification
Professional MATLAB source code with comprehensive documentation and examples
MATLAB Example Implementation of RBF Neural Network for Function Approximation and Pattern Classification
Face detection algorithm utilizing AdaBoost methodology, featuring complete training pipeline with optimized data reading structure to reduce memory consumption
A comprehensive clustering analysis toolbox developed by Dr. Alexander, featuring a wide range of clustering algorithms and visualization capabilities for data mining and pattern recognition tasks
This artificial immune algorithm source code implements a complete optimization process including: 1. Parameter initialization 2. Random population generation using pop=initpop(popsize, chromlength) 3. Fault type encoding with code(1,:) for normal, c
MATLAB examples for BP Neural Networks (primarily used for function approximation and pattern classification) with detailed code implementation
MATLAB program implementing genetic algorithm for set covering problem, which utilizes row-based description with 0-1 variables and features chromosome encoding, fitness evaluation, and selection operations.
Implementation of genetic algorithm for optimizing wind turbine blade aerodynamic profiles, achieving rapid and efficient design processes with computational intelligence techniques
Implementation of the Backpropagation (BP) algorithm for a three-layer feedforward neural network. The program includes the following key features: (1) Configurable node counts for each layer (input, hidden, output); (2) Adjustable learning rate η fo
This program implements fuzzy clustering algorithms for image classification tasks, specifically designed for processing 30x30 pixel images with enhanced feature extraction and cluster optimization capabilities.
MATLAB code for RBF neural networks supporting both classification and regression applications with detailed implementation insights
Bacterial Foraging Algorithm simulates the foraging behavior of E. coli bacteria in the human intestine, belonging to the category of bio-inspired optimization algorithms. In the BFA model, solutions to optimization problems correspond to bacterial s
A highly effective toolbox utilizing genetic algorithms to optimize neural network weights, addressing global convergence issues and delivering rapid training performance
Deep learning typically adopts a hierarchical learning structure, which is theoretically grounded in simulating the workings of the human brain's cerebral cortex. The visual region of the cerebral cortex also operates hierarchically, with lower-level
MATLAB-based implementation of ant colony algorithm for continuous function optimization, including comprehensive documentation files and research papers with code examples and parameter explanations.
Comprehensive documentation on RBF neural networks for system identification, featuring practical code examples and implementation techniques
Implementing digital and alphanumeric recognition with BP neural networks provides significant reference value for researchers working on license plate recognition systems, featuring practical code implementation insights.
This implementation employs a Genetic Algorithm (GA) to optimize point-to-point trajectory planning for a 3-link robotic arm, with the objective function minimizing travel time and distance while adhering to predefined maximum torque constraints.
This well-structured MATLAB immune algorithm source code offers practical functionality and reliable performance, featuring comprehensive implementation of key optimization mechanisms including clonal selection, antibody diversity maintenance, and af
This collection includes 13 different particle swarm optimization algorithms that are all executable, featuring implementation approaches like velocity update mechanisms and position boundary handling. Each algorithm comes with straightforward MATLAB
The hill climbing algorithm effectively addresses various function optimization challenges and can be integrated with other optimization techniques like ant colony optimization and particle swarm optimization, demonstrating significant research value