Explore MATLAB source code curated for "识别率" with clean implementations, documentation, and examples.
A MATLAB implementation of the Dynamic Time Warping (DTW) algorithm achieving 100% recognition accuracy, featuring direct usability and seamless conversion to C code for embedded system deployment.
This program implements character recognition using Linear Discriminant Analysis (LDA) method, achieving high recognition accuracy with multiple experimental results provided.
A speaker recognition program designed to identify 10 distinct speakers with 90% recognition accuracy. Execute "laststep.m" to run the complete system, which incorporates feature extraction and pattern matching algorithms.
This user-friendly program features an intuitive graphical interface for loading images and training Hopfield neural networks. The implementation allows adding noise to test pattern recognition rates, employing Hebbian learning rules for weight matrix computation and asynchronous updates for state convergence.
Character recognition based on Backpropagation Neural Networks. The BP neural network algorithm transforms input-output sample problems into nonlinear optimization problems and solves weight values through iterative gradient descent operations. This implementation uses BP networks for classification with supplemental linear perceptrons for effective single-character recognition. The algorithm features straightforward implementation, high recognition accuracy, and robust performance in various high-noise environments for printed character recognition. Key implementation aspects include gradient computation, weight updating mechanisms, and activation function configuration.
Face recognition implementation based on Linear Discriminant Analysis (LDA), featuring experimental validation on the ORL face database to evaluate algorithm recognition rates with detailed code implementation and parameter optimization strategies.
MATLAB-based image recognition program for crop diseases and pests, trained on four severity levels, achieving high accuracy after model completion with implementation featuring image preprocessing, feature extraction, and machine learning classification.
The ICA face recognition method achieves high recognition rates with optimized algorithms and demonstrates significant practical value in real-world applications.
Handwritten digit recognition system featuring a graphical user interface capable of accurate single-digit identification with high recognition accuracy, utilizing machine learning algorithms for robust performance
A program utilizing short-time zero-crossing rate for voice activity detection, achieving high recognition accuracy in endpoint detection processes with robust signal processing techniques