The Significant Logistic Map in Chaos Theory
This program implements the crucial Logistic Map from chaos theory, featuring a plotting function that visually demonstrates fascinating chaotic phenomena through generated curves.
Lyapunov Exponent - A Critical Concept in Chaos Theory
Practical implementation guide for calculating the Lyapunov exponent in chaos theory, including mathematical formulas and runnable code examples with algorithm explanations
Calculation Method for Lyapunov Exponents in Chaos Theory
Implementation of Lyapunov exponent calculation methods in chaos theory using MATLAB software, including partial code annotations and algorithm explanations
Significant Cellular Neural Network in Chaos Theory
This program implements an important cellular neural network in chaos theory, featuring visualization capabilities that clearly demonstrate fascinating hyperchaotic attractors through dynamic curve plotting.
MATLAB Simulation for Water Quality Prediction Using SVM and Chaos Theory
MATLAB simulation program for water quality prediction implementing Support Vector Machines (SVM) and chaos theory, featuring comprehensive documentation with algorithm explanations and code structure details. This graduation project includes robust data preprocessing, phase space reconstruction, and predictive modeling components suitable for environmental research applications.
Short-Term Load Forecasting Using Chaos Theory and Generalized Regression Neural Network
This approach combines chaos theory with generalized regression neural networks for short-term load forecasting, achieving satisfactory predictive performance through nonlinear system analysis and machine learning implementation.
Short-Term Load Forecasting Based on Chaotic Theory and Elman Neural Network
This program implements short-term load forecasting using chaotic theory and Elman neural networks, delivering excellent prediction accuracy. It provides a ready-to-use solution for power system short-term load forecasting and can be equally applied to other time series prediction tasks. The implementation features phase space reconstruction for chaotic analysis and Elman's recurrent neural network architecture with feedback connections for capturing temporal dependencies.
Bifurcation Diagram in Chaos Theory with Source Code Implementation
This source code generates a bifurcation diagram for chaos theory, demonstrating the transition from orderly behavior to chaotic dynamics through parameter variation. The implementation allows analysis of dynamical system characteristics using numerical simulation approaches with adjustable control parameters and iterative mapping functions.
Phase Space Reconstruction Function (PSR) for Chaotic Time Series Analysis
PSR Function Implementation for Chaotic System Phase Space Reconstruction
MATLAB Simulation of Water Quality Prediction Based on SVM and Chaos Theory
MATLAB simulation of water quality prediction integrating Support Vector Machine (SVM) and chaos theory, featuring nonlinear time series analysis and predictive modeling