Online Dictionary Learning Algorithms
Implementation of online dictionary learning algorithms for processing high-dimensional feature data, with comprehensive code examples and optimization techniques.
A-Star Algorithm Path Planning with Code Implementation Guide
Comprehensive guide to A* algorithm path planning featuring detailed explanations, code implementation examples, and practical applications - ideal for beginners learning pathfinding algorithms.
FWNN Implementation Source Code for Fuzzy Wavelet Neural Network
Source code implementation for FWNN (Fuzzy Wavelet Neural Network) using GD (Gradient Descent) learning algorithm with comprehensive neural network architecture details
Neural Network PID Control Strategy
This paper discusses neural network PID control strategy, proposing a single-neuron adaptive PID controller with its control model. It explores the learning algorithm for single-neuron adaptive PID control, constructing an adaptive PID controller by modifying neuron connection weight coefficients. The self-learning capability of neural networks enables online tuning of PID control parameters. MATLAB simulations compare traditional PID controllers with single-neuron adaptive PID controllers, demonstrating that neural network PID controllers offer simplified parameter adjustment, high precision, strong adaptability, and satisfactory control performance.
SVR (Support Vector Regression) - Machine Learning Algorithm for Predictive Modeling
SVR (Support Vector Regression) is a supervised learning algorithm based on support vector machines, primarily used for regression tasks and data prediction. It employs kernel functions to handle non-linear relationships and demonstrates robustness against outliers through epsilon-insensitive loss functions.
Error Learning Algorithm for Artificial Neural Network Perceptrons
Implementation of the error learning algorithm for single-layer perceptrons in artificial neural networks with code-level insights
Extreme Learning Machine (ELM): Algorithm Overview and Implementation
Extreme Learning Machine (ELM) is an efficient and user-friendly learning algorithm for Single-hidden Layer Feedforward Neural Networks (SLFNs). Proposed by Associate Professor Guang-Bin Huang at Nanyang Technological University in 2006, ELM eliminates the need for manual hyperparameter tuning common in traditional neural network algorithms like Backpropagation (BP). The algorithm requires only the specification of hidden layer nodes, automatically determines input weights and biases, and guarantees unique optimal solutions with exceptional learning speed and generalization performance. Key implementation involves random weight initialization and Moore-Penrose pseudoinverse computation for output weight derivation.
Extreme Learning Machine (ELM): A Fast Neural Network Algorithm for Single-Hidden Layer Feedforward Networks
Extreme Learning Machine (ELM) is a simple, efficient learning algorithm for Single-hidden Layer Feedforward Networks (SLFNs) proposed by Associate Professor Guang-Bin Huang from Nanyang Technological University in 2006. Unlike traditional neural network training algorithms (e.g., Backpropagation), ELM requires minimal parameter tuning—only the number of hidden nodes needs specification—and avoids local optima by randomly initializing input weights and biases without iterative adjustments. The algorithm computes output weights analytically via Moore-Penrose pseudoinverse, ensuring unique optimal solutions and delivering rapid training with strong generalization. Code implementations typically involve random weight initialization, hidden layer activation (e.g., sigmoid), and linear output solving.
An Efficient Learning Algorithm for Sparse Bayesian Models Implementation
A MATLAB function package implementing an effective learning algorithm for sparse Bayesian models with comprehensive code examples and optimization techniques