Generation of Typical Chaotic Time Series
This program implements generation procedures for several benchmark chaotic time series, including Rossler, Chen, Logistical, and Lorenz sequences, with detailed code implementation insights.
MATLAB Time Series Modeling, Prediction, and Implementation with Program Code
1) Generate a .wav file by encoding your contact phone number using DTMF (Dual-Tone Multi-Frequency) signaling with MATLAB. Implementation typically involves using the `audiowrite` function and generating dual-frequency sine waves for each digit. 2) Decode the generated DTMF file to extract the encoded telephone number. This requires implementing a Goertzel algorithm or FFT-based frequency detection to identify the characteristic tone pairs in the audio signal.
Complexity Testing for Time Series with Fast Approximate Entropy Algorithm
Implementation of fast approximate entropy algorithm for time series complexity analysis, capable of processing both deterministic and random signals with significantly improved computational efficiency
MATLAB Source Code for Modulus Maxima Detection Algorithm
MATLAB source code implementation for modulus maxima detection, enabling identification of singularity points in time series data and subsequent signal reconstruction through multiscale wavelet analysis.
Time Series Prediction with Dynamic Neural Networks - MATLAB-Based NARX Implementation
Time series forecasting holds significant importance in economics and engineering fields. This study leverages the characteristics of dynamic neural networks to propose a time series prediction methodology, implementing a designed dynamic network to forecast response time series of Duffing's equation. Results demonstrate that dynamic neural networks effectively predict response time series of dynamic systems, with MATLAB implementation utilizing NARX (Nonlinear Autoregressive with Exogenous Input) network architecture and time-delay feedback mechanisms.
Generation of Several Typical Chaotic Time Series
Implementation of several typical chaotic time series generation programs with corresponding attractor visualization plots for each chaotic system
Optimizing Time Delay for Chaotic Time Series Phase Space Reconstruction Using Mutual Information Method
MATLAB implementation for calculating optimal time delay in chaotic time series phase space reconstruction through mutual information method
Time Series Prediction Using Neural Networks Optimized with Genetic Algorithm
Implementation of genetic algorithm-optimized neural networks for time series prediction. The genetic.m interface function provides straightforward configuration, allowing direct modification of neural network parameters. Users can easily substitute their own data files by updating the load function call, enabling efficient adaptation to diverse datasets.
Chaotic Time Series Prediction Using Sugeno-Type ANFIS
Implementation of chaotic time series prediction using Sugeno-type Adaptive Neuro-Fuzzy Inference System (ANFIS).
Time Series Chaos Analysis for Graduation Research
Graduation project focusing on time series chaos analysis, implementing phase space reconstruction, Lyapunov exponent calculation, wavelet denoising techniques with Python/MATLAB code examples