Prediction Model Based on Extreme Learning Machine
Extreme Learning Machine-based prediction model utilizing chaotic data for enhanced accuracy and robustness
Calculating Correlation Coefficients in Mathematical Modeling
Code implementation for computing correlation coefficients between datasets in mathematical modeling applications
MATLAB Implementation of K-Means Clustering Algorithm
Develop a K-means clustering algorithm program to perform cluster analysis on the data shown in the figure below (select k=2), including centroid initialization and iterative optimization steps
Fuzzy Kernel Clustering: Algorithm Implementation and Applications
Implementation of fuzzy kernel clustering with supporting research papers for effective fuzzy clustering segmentation of data and images, demonstrating satisfactory performance in practical applications
MATLAB Implementation of BP Neural Network for Wind Turbine Fault Diagnosis
Implementation of BP Neural Networks and Support Vector Machines for wind turbine fault diagnosis and classification, complete with sample dataset for training and testing
Support Vector Machine Classification Algorithm Implementation
Implementing support vector machine classification algorithm using MATLAB, with comprehensive data training and testing procedures to achieve accurate data categorization
Backpropagation Neural Network for Prediction using MATLAB
A MATLAB implementation of BP neural network for predicting various types of data with detailed code structure and algorithm explanations
Time Series Prediction Using MATLAB Wavelet Neural Networks
Time series refers to a sequence of data points arranged at specific time intervals, representing various metrics such as product demand, production volume, or sales figures. The intervals can be measured in any time unit (hours, days, weeks, months). When establishing relationships with dependent variables proves difficult or data collection is challenging, regression analysis may not be suitable. For cases where high prediction accuracy isn't critical, time series analysis offers an effective alternative. Implementation typically involves preprocessing data using wavelet decomposition (e.g., MATLAB's wavedec function) to extract features, followed by neural network training with functions like feedforwardnet for pattern recognition and forecasting.
MATLAB Source Code for SVM Classification
Implementation of SVM classification using MATLAB source code, including a sample dataset for simulation experiments with detailed algorithm explanations.
Loaded ORL and YALE Face Database Data for Machine Learning
Pre-processed ORL and YALE face database datasets ready for machine learning applications. When loaded using LODA, the 'train' variable contains training samples while 'test' represents testing samples, following standard dataset partitioning for model training and evaluation.