Explore MATLAB source code curated for "模式识别" with clean implementations, documentation, and examples.
Custom-developed PSO-optimized RBF neural network program with guaranteed functionality. This implementation features parameter optimization through particle swarm algorithms for improved prediction accuracy. Suitable for data forecasting and pattern recognition applications with customizable adaptation options.
Classic MATLAB implementation of Hidden Markov Model (HMM) algorithm for pattern recognition and artificial intelligence applications, featuring complete training and recognition modules
Face recognition, as a complex pattern recognition problem, has garnered widespread attention in recent years, with various methods in the recognition field demonstrating their strengths and leading to the development of many novel approaches that significantly enrich and broaden the direction of pattern recognition. This project utilizes an image database containing facial images from different angles, comprising 10 individuals with 5 images each, depicting face orientations: left, left-front, front, right-front, and right. An LVQ neural network is created to predict and recognize the orientation of any given facial image. Compared to BP neural networks, LVQ networks require no data preprocessing and directly compute distances between input vectors and competitive layers for pattern recognition. Recent years have seen increasingly in-depth research on LVQ neural networks, with applications becoming more widespread.
Hierarchical clustering in pattern recognition with practical MATLAB code examples using custom implementations instead of built-in functions, covering algorithm principles and key computational steps
This implementation employs a three-layer BP neural network with optimized architecture using empirical formulas for hidden layer node calculation and parameter tuning, achieving robust handwritten digit recognition through systematic training and image preprocessing techniques.
This comprehensive pattern recognition assignment covers Parzen window principles, algorithm implementation, and source code. Includes detailed result analysis, performance evaluation, and feasibility studies with practical code examples and mathematical formulations.
MATLAB implementation of BP neural network for function approximation and pattern classification tasks
Overview of key pattern recognition algorithms featuring detailed implementation processes, experimental reports, and code-level explanations of core functions and methodologies
A pattern recognition based license plate recognition system capable of processing all images in the image library with robust detection and extraction algorithms
MATLAB implementation of Parzen window estimation for pattern recognition with density estimation and classification capabilities