Implementation of RBF Neural Network Using K-Means Clustering Learning Algorithm
Implementation of RBF Neural Network Using K-Means Clustering Algorithm with Code Integration and Parameter Optimization
Implementation of RBF Neural Network with K-Means Clustering Algorithm in MATLAB Environment
Implementation of RBF Neural Network Integrated with K-Means Clustering Algorithm in MATLAB Environment for Pattern Recognition and Data Classification
Optimizing RBF Neural Network Architecture Using Genetic Algorithms
Utilizing genetic algorithms to optimize RBF neural network structures, including weight optimization and Gaussian basis function center/width tuning, with implementation insights for parameter encoding and fitness evaluation.
RBF Neural Network Classifier with 4-Input Layer, 3-Hidden Layer, and 2-Output Layer Architecture
MATLAB implementation of a Radial Basis Function (RBF) neural network classifier featuring a 4-3-2 layer configuration, complete with training and testing datasets for performance evaluation
The RBF Neural Network: A Three-Layer Feedforward Architecture with a Single Hidden Layer
The RBF (Radial Basis Function) neural network is a three-layer feedforward structure consisting of an input layer, a hidden layer, and an output layer. This code implementation focuses on constructing and training an RBF neural network model, featuring algorithmic explanations for key components such as radial basis function calculations, weight optimization, and training methodologies.
Optimizing RBF Neural Network Weights Using Particle Swarm Algorithm
Enhancing RBF neural networks' nonlinear function approximation capabilities through particle swarm optimization of network weights, with implementation insights into algorithm integration and weight updating mechanisms
Sliding Mode Control with Adaptive Learning of Upper Bound via RBF Neural Network
This program implements sliding mode control with adaptive learning of upper bounds using RBF neural networks, designed for scenarios where upper bound values cannot be practically measured. The implementation includes neural network-based estimation algorithms and adaptive control law adjustments.
Optimization of RBF Neural Networks Using Adaptive Genetic Algorithm
Comparative analysis of simulation results between adaptive genetic algorithm-optimized RBF neural networks and particle swarm optimization-optimized RBF neural networks, featuring directly executable MATLAB code implementations.
Simulation Comparison Between BP Neural Networks and RBF Neural Networks with MATLAB Implementation
This project presents a comparative simulation study between Backpropagation (BP) and Radial Basis Function (RBF) neural networks. The package contains four MATLAB scripts that demonstrate different aspects of both network architectures, including training algorithms, performance evaluation, and practical applications.
RBF Neural Network Chaos Prediction Control with MATLAB Implementation
Chaos prediction control using RBF neural networks with complete MATLAB source code for system implementation and testing