Explore MATLAB source code curated for "模糊" with clean implementations, documentation, and examples.
Implementation of Genetic Algorithm for Optimizing PID Controller Parameters in Electric Motor Control Systems using AI Techniques
Dynamic Fuzzy Neural Network with Automatic Rule Generation Capability. Derived from Professor Wu Shiqian's book "Dynamic Fuzzy Neural Networks" at Tsinghua University, featuring adaptive learning algorithms and self-organizing network structures.
This code implements image thresholding using fuzzy c-means clustering, demonstrating superior performance compared to traditional Otsu's method for various image types.
Implementation of Discrete Cosine Transform (DCT) high-pass and low-pass filters for image processing, with comparison to Fourier Transform (FFT) filtering. Visual results demonstrate that DCT low-pass filtering produces significant blurring due to energy reduction from taking the real component of FFT. DCT high-pass filtering removes low-frequency components, resulting in darkened images with only edge traces visible. Code implementation includes frequency domain masking and coefficient thresholding techniques.
Comprehensive programs and implementations for Support Vector Machine classification, regression, and fuzzy SVM approaches with practical code examples
RBF networks face challenges in determining hidden layer node centers and basis width parameters; they possess unique optimal approximation properties without local minima. However, their Gaussian activation functions exhibit localized characteristics. We implement function approximation using fuzzy RBF networks, which effectively handle fuzzy data and uncertainty through integrated membership functions and rule-based reasoning mechanisms.
Simulation files and fuzzy rules for adaptive fuzzy PID control implementation
Wavelet-based image denoising program designed to process blurry image files by removing noise and enhancing resolution through multi-scale analysis and thresholding techniques.
The objective of image enhancement is to improve picture quality by increasing contrast, reducing blur and noise, and correcting geometric distortions, while image restoration is a technique that estimates the original image assuming known models of blur or noise. Image enhancement methods are categorized into frequency domain and spatial domain approaches. Frequency domain methods treat images as 2D signals and employ 2D Fourier transform for signal enhancement, with low-pass filtering to remove noise and high-pass filtering to sharpen edges. Spatial domain algorithms include local averaging and median filtering (using the median pixel value in a local neighborhood) for noise reduction.
Adaptive fuzzy PID control program requiring FIS file import for configuration and operation