LSSVM Source Code for Modeling and Prediction
This LSSVM source code provides an excellent toolkit for modeling and prediction tasks, featuring remarkable convenience, simplicity, and practical implementation with well-structured code organization
Optimizing LSSVM Parameters Using Genetic Algorithm (GA)
Implementing GA-based parameter optimization for LSSVM in MATLAB platform, featuring practical code examples and algorithm explanations.
Intelligent Particle Swarm Optimization for Parameter Tuning in Least Squares Support Vector Machines
Example Program Demonstrating LSSVM Parameter Optimization Using Particle Swarm Intelligence (LSSVM+PSO Implementation)
MATLAB Implementation of LSSVM with Particle Swarm Optimization
LSSVM program utilizing Particle Swarm Optimization algorithm for model parameter tuning with enhanced convergence and generalization capabilities
LSSVM Least Squares Simulation
This is an LSSVM least squares simulation program featuring MATLAB-based implementation with kernel function selection and parameter optimization capabilities. The code demonstrates practical applications of LSSVM through regression and classification examples.
Support Vector Machine (SVM) Theoretical Foundation: Statistical Learning Theory and SVM Principles
High-quality research paper with accompanying source code. This study first reviews the application research status of load forecasting, summarizes the characteristics and influencing factors of load forecasting, categorizes common methods for short-term load forecasting, and analyzes the advantages and disadvantages of various methods. It then introduces the statistical learning theory as the theoretical foundation of Support Vector Machines (SVM) and explains SVM principles, deriving the SVM regression model. The paper employs a Least Squares Support Vector Machine (LSSVM) model, utilizing historical load data and meteorological data from Taizhou, Zhejiang Province to analyze various factors affecting predictions and summarize load variation patterns. The implementation includes preprocessing steps such as correcting "abnormal data" in historical load records and normalizing relevant factors for load forecasting. The study specifically addresses the significant impact of two key parameters in LSSVM models, which are currently determined empirically. The methodology incorporates parameter optimization using Particle Swarm Optimization (PSO) algorithm, where test set error serves as the criterion for parameter selection, demonstrating improved prediction accuracy through systematic parameter tuning.
Least Squares Support Vector Machine (LSSVM) - Core Implementation
Main program implementation of Least Squares Support Vector Machines (LSSVM) featuring fundamental classification and regression capabilities with configurable kernel functions and optimization parameters
QPSO Quantum Particle Swarm Optimization Algorithm
Implementation and Optimization of Quantum-behaved Particle Swarm Algorithm
LSSVM - Least Squares Support Vector Machine
LSSVM - Least Squares Support Vector Machine Implementation and Algorithm Overview