Explore MATLAB source code curated for "BP" with clean implementations, documentation, and examples.
The ELM algorithm for neural networks demonstrates faster performance than traditional BP and SVM methods while maintaining high accuracy. Implemented in MATLAB, this version includes modifications to support diverse functions and automatically generates classification matrices during data processing. The implementation features optimized matrix operations for hidden layer computation and efficient weight calculation algorithms.
Implementation of BP Neural Network-based digit recognition system for numbers 0-9 in various fonts using MATLAB, featuring complete code architecture and performance analysis
This implementation combines Particle Swarm Optimization (PSO) with its rapid convergence characteristics and Backpropagation Neural Networks (BPNN) with strong global search capabilities. The program has been successfully debugged and demonstrates superior performance through the integration of these two algorithms, featuring optimized parameter initialization and adaptive learning rate mechanisms.
Implementation-ready prediction commands applicable to BP, Elman, RBF, and GRNN neural networks with algorithmic enhancements
MATLAB source code for fetal ECG signal extraction based on RBF (Radial Basis Function), BP (Backpropagation), and ANFIS (Adaptive Neuro-Fuzzy Inference System) artificial neural networks with algorithm implementation details
Neural network source code implementable for remote sensing image classification, featuring methods including Backpropagation (BP) and Kohonen algorithms with detailed architecture specifications.
A comparative analysis of reconstruction errors for CoSaMP, Basis Pursuit (BP), and Orthogonal Matching Pursuit (OMP) algorithms under varying signal-to-noise ratio (SNR) conditions, with computational implementation insights.
Four compressed sensing reconstruction algorithms: StOMP (Stagewise Orthogonal Matching Pursuit), BP (Basis Pursuit), OMP (Orthogonal Matching Pursuit), and block_omp (Block Orthogonal Matching Pursuit) with implementation insights
This collection contains 30 practical MATLAB neural network case studies with executable programs, covering BP, RBF, SVM, SOM, Hopfield, LVQ, Elman, wavelet networks, and extending to optimization techniques like PSO (Particle Swarm Optimization), grey neural networks, fuzzy networks, probabilistic neural networks, and genetic algorithm implementations.
Comparative simulation analysis of LDPC LLR BP decoding algorithm in AWGN channels with MATLAB implementation insights