Explore MATLAB source code curated for "人工智能" with clean implementations, documentation, and examples.
Implementation of Genetic Algorithm for Optimizing PID Controller Parameters in Electric Motor Control Systems using AI Techniques
Implementation of the classic c-means algorithm in artificial intelligence with MATLAB matrix-based data storage, offering superior efficiency compared to VC implementations
Source code implementation of MATLAB algorithms for artificial immune systems, widely used in artificial intelligence with significant educational value for studying immune-inspired computing techniques.
A doctoral-researcher developed toolkit for PDE-based image processing. Highly practical and beneficial for professionals working with partial differential equations in image analysis. Includes implementations of key algorithms like Perona-Malik diffusion and variational methods.
Distance-based feature selection with genetic algorithm optimization for AI and pattern recognition applications, implementing dimensionality reduction through fitness evaluation and chromosome operations.
This MATLAB implementation of the Firefly Algorithm, belonging to swarm intelligence algorithms, was developed as a university project. The code features a main program with multiple functions simulating fundamental firefly behaviors, making it highly applicable in artificial intelligence optimization problems.
Trajectory planning for aircraft is a key technology in achieving autonomous navigation, representing an important research direction in artificial intelligence and guidance/navigation fields. This provides a systematic and in-depth introduction to the concepts, theories, and methods of unmanned aerial vehicle trajectory planning, with code implementation insights.
Support Vector Machine (SVM) classification algorithms represent a relatively recent advancement in pattern recognition within artificial intelligence, offering robust solutions for complex classification tasks through kernel-based transformations and hyperplane optimization.
Classic MATLAB implementation of Hidden Markov Model (HMM) algorithm for pattern recognition and artificial intelligence applications, featuring complete training and recognition modules
Leveraging the cloud model in artificial intelligence, this approach utilizes the forward and backward generation techniques of normal cloud generators to achieve comprehensive floral information extraction and reconstruction