Explore MATLAB source code curated for "BP" with clean implementations, documentation, and examples.
Data prediction based on Generalized Regression Neural Network (GRNN), featuring cross-validation GRNN neural network prediction program with comparative analysis between BP and GRNN networks. Both networks are trained using identical datasets, demonstrating implementation approaches for neural network optimization and performance evaluation.
Various classic compressed sensing reconstruction algorithms including Basis Pursuit (BP), Orthogonal Matching Pursuit (OMP), Compressive Sampling Matching Pursuit (CoSaMP), Linear Programming (LP) approaches, and more, with implementation insights.
Basis Pursuit (BP), similar to Matching Pursuit (MP), is a method for achieving signal sparse decomposition with distinct optimization-based implementation approaches
Implementation of Backpropagation Algorithm for Digital Character Recognition using Neural Networks
A comprehensive toolkit for SAR imaging processing, featuring multiple algorithms including Polar Format Algorithm (PFA), Range Migration Algorithm (RMA), Backprojection (BP), and Chirp Scaling Algorithm (CSA).
Implementation of three neural network methods for handwritten character recognition - Probabilistic Neural Network (PNN), Radial Basis Function Network (RBF), and Backpropagation Neural Network (BP) with code-level insights
MATLAB implementations for compressed sensing image processing, featuring algorithms including CoSaMP (Compressive Sampling Matching Pursuit), GBP (Gradient-Based Pursuit), IHT (Iterative Hard Thresholding), OMP (Orthogonal Matching Pursuit), BP (Basis Pursuit), and SP (Subspace Pursuit) with detailed code structure explanations
Performance comparison of LDPC decoding algorithms including Belief Propagation (BP), Min-Sum Algorithm, and Brute-Force (BF) Method with implementation insights
This resource provides practical implementations of BP (Backpropagation) neural network and PID (Proportional-Integral-Derivative) control algorithms. These implementations demonstrate key concepts through MATLAB/Python code examples, including error calculation and parameter tuning methods.
Multiple MATLAB demonstration programs for neural networks, including self-written and modified implementations of BP, RBF, PNN, Hopfield, and other network architectures