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MATLAB Implementation of Generalized Predictive Control Algorithm with Simulation Examples Using Step and Sine Functions, Including Code Implementation Details and Algorithm Explanations
A MATLAB-implemented adaptive genetic algorithm example with performance improvements compared to traditional approaches, featuring automatic parameter adjustment and enhanced optimization capabilities
Decomposing multi-class problems into multiple binary-class subproblems is a common approach for multi-class classification. Traditional One-Against-All (OAA) decomposition relies more on individual classifier accuracy than diversity. This paper intr
Implementation of adaptive image enhancement through parameter optimization using quantum genetic algorithms, with code-level descriptions of quantum bit representation and fitness evaluation mechanisms.
Implementing support vector machine classification algorithm using MATLAB, with comprehensive data training and testing procedures to achieve accurate data categorization
Application of genetic algorithms for power grid planning, specifically in transmission network optimization. This research proposes a genetic algorithm-based transmission network planning model that minimizes the combined cost of new line investment
Basic backpropagation neural network program featuring flexible hidden layer configuration, with implementation details for pattern recognition and predictive modeling.
Solving project scheduling problems using genetic algorithm integrated with critical chain methodology
BP neural network can predict future population values by analyzing population data from previous years, utilizing backpropagation algorithm for model training and optimization.
An enhanced ant colony optimization clustering algorithm implementation demonstrating superior performance compared to basic ant colony clustering methods.
Implementation of BP Neural Networks and Support Vector Machines for wind turbine fault diagnosis and classification, complete with sample dataset for training and testing
A novel swarm intelligence optimization algorithm featuring simplicity and high efficiency for solving complex optimization problems with nature-inspired cooperative behavior modeling.
This MATLAB code implements a cost-sensitive Support Vector Machine (SVM) model optimized by Particle Swarm Optimization (PSO) algorithm, specifically designed for handling imbalanced datasets through automated parameter tuning.
Materials on Simulated Annealing Algorithm with Implementation Details
Classical iterative histogram separation clustering algorithm for multi-class object segmentation, leveraging image histogram analysis for high computational efficiency suitable for large-scale data processing
Pattern recognition of handwritten English letters with a GUI interface demonstrating the recognition workflow and algorithmic steps.
Applying Backpropagation Neural Networks to Pursue Optimal Algorithms for Wind Turbine Optimization
Implementation of Bayesian pattern recognition on the University of Wisconsin's long-term breast cancer dataset using Gaussian models, achieving over 95% recognition rate. This serves as a significant case study in pattern recognition with practical
This MATLAB demonstration showcases Support Vector Machine (SVM) Regression implementation, covering algorithm configuration, kernel selection, and practical applications with real-world datasets.
Combining the advantages of Particle Swarm Optimization and Harmony Search algorithms, this approach effectively merges swarm intelligence principles with stochastic global search techniques, validated through multiple function optimization problems