Professional MATLAB source code with comprehensive documentation and examples
Moth-Flame Optimization (MFO) is a novel meta-heuristic algorithm inspired by the biological behavior of moths navigating at night using transverse orientation. Proposed in 2015, it solves optimization problems by tracking optimal positions and imple
This implementation performs image segmentation using Support Vector Machine (SVM) after obtaining two-class segmented images through K-means clustering. The algorithm involves interactive point selection where users collect 2*num coordinate position
Solution algorithms for deep learning theory in neural networks, with methods to uncover structural information from data through computational approaches
MATLAB-based SVM source code implementation for support vector machine functionality, designed for feature classification and extraction tasks
Constrained Environment/Economic Dispatch - Easy to Understand Implementation, Recommended for High-Version MATLAB Usage with Advanced Optimization Features
License Plate Recognition - A highly practical technology involving plate extraction and neural network training with comprehensive implementation approaches.
RBF Neural Network Model enables rapid deployment and offers straightforward implementation with user-friendly integration capabilities
Utilizing genetic algorithm toolbox for tuning and optimizing PID controller's three parameters, providing significant assistance for beginners in intelligent PID tuning optimization with practical implementation examples
High-value practical implementation combining genetic algorithm optimization with backpropagation neural networks in MATLAB, featuring complete source code with data examples and documentation.
RBF prediction function demonstrating excellent approximation capabilities with high precision for time series forecasting and pattern recognition applications
Implementation of three-dimensional path planning algorithm based on ant colony optimization with complete code. Contains detailed tutorial explaining algorithm mechanics and code structure. For high-resolution tutorial files, please contact 10661466
A comprehensive set of practical and user-friendly heuristic optimization algorithms, including non-adaptive algorithms, simulated annealing-based population algorithms, basic genetic algorithms, differential evolution algorithms, and particle swarm
Implementing stock selection analysis by applying Data Envelopment Analysis (DEA) and Genetic Algorithm (GA) to fundamental data of listed companies. The genetic algorithm optimizes the selection of fundamental indicators, while DEA evaluates stock e
Implementation of hybrid algorithm programs combining genetic algorithms with neural networks and particle swarm optimization with neural networks, enabling comparative analysis of algorithmic performance across different optimization and machine lea
The Mean Shift-based image segmentation process involves clustering image pixels using the Mean Shift algorithm, where starting points converging to the same mode are grouped into one class and assigned corresponding labels. Classes with insufficient
Hybrid programming combining genetic algorithms and neural networks for optimization computations, including implementation approaches and technical considerations. For reference purposes only.
MATLAB Toolbox for Simulated Annealing Algorithm Implementation with Customizable Parameters
Deep Learning Convolutional Neural Networks (CNN) with detailed annotations and code examples - An excellent reference resource featuring implementation walkthroughs and architecture explanations
Digital Image Processing, Pattern Recognition, and Face Recognition with included programs and face database - an excellent learning resource with practical implementations
MATLAB source code for 30 intelligent algorithm case studies, accompanying the book "MATLAB Intelligent Algorithm 30 Case Studies" with detailed implementation examples