Artificial Immune Algorithm and Neural Network for Bearing Pattern Recognition and Fault Diagnosis
Implementation of Artificial Immune Algorithm and Neural Network for Bearing Pattern Recognition and MATLAB-based Fault Diagnosis with Code Examples
MATLAB Implementation of Artificial Immune Algorithm for Fault Diagnosis
MATLAB-based artificial immune algorithm implementation with applications in fault diagnosis systems, featuring immune-inspired optimization mechanisms and pattern recognition capabilities.
MATLAB Implementation of Wavelet Neural Networks
A wavelet neural network program derived from analog circuit fault diagnosis applications, provided for learning and reference purposes with detailed code implementation insights
Support Vector Data Description for Fault Diagnosis with Comprehensive Annotations and References
Implementation of Support Vector Data Description for fault diagnosis, featuring extensive code annotations, detailed algorithm explanations, and comprehensive reference documentation
MATLAB Code Implementation for Backpropagation Network Training
Backpropagation network training source code suitable for parameter identification and pattern classification applications including damage detection and fault diagnosis
BP Neural Network Code for Fault Diagnosis
BP Neural Network implementation for fault diagnosis, featuring data normalization processing and network parameter optimization strategies
Improved Backpropagation Algorithm for Diesel Engine Fault Diagnosis with MATLAB Implementation
MATLAB implementation of an enhanced backpropagation algorithm for diesel engine fault detection and diagnosis, featuring optimized neural network training with gradient descent adjustments and activation function improvements for engineering students and researchers.
MATLAB Simulation Program for Fault Diagnosis Using PCA Algorithm
MATLAB simulation program implementing Principal Component Analysis (PCA) for fault detection and diagnosis systems, featuring algorithm demonstration and performance optimization
Implementation of Clonal Selection Algorithm in Immune Algorithm for Fault Diagnosis
The clonal selection algorithm within immune algorithm frameworks demonstrates excellent performance in fault diagnosis applications after implementation, leveraging pattern recognition and adaptive learning capabilities.
OSELM Code Implementation: Online Sequential Extreme Learning Machine
The Online Sequential Extreme Learning Machine (OS-ELM) operates through two key phases: Initialization Phase - trains on limited fault data using ELM methodology, discards training data after learning, and stores parameters H (hidden layer output matrix) and β (output weight matrix) in the network; Online Learning Phase - dynamically updates parameters H and β using streaming fault data, continuously enhancing classification performance and generalization capability for improved fault diagnosis accuracy. The trained OS-ELM parameters stored in network nodes enable cross-platform deployment - the weight parameter β can be loaded onto new PCs, DSPs, ARM-based embedded systems, etc. For new fault test data, only the corresponding hidden layer output matrix H needs regeneration.