Phase Space Reconstruction Parameter Selection: Delay Time and Embedding Dimension
Parameter Selection for Phase Space Reconstruction: Delay Time and Embedding Dimension with Implementation Approaches
Explore MATLAB source code curated for "延迟时间" with clean implementations, documentation, and examples.
Parameter Selection for Phase Space Reconstruction: Delay Time and Embedding Dimension with Implementation Approaches
Compute delay time with mutual information method; supports custom data input for efficient processing with embedded algorithm implementation.
MATLAB implementation of delay time and correlation dimension calculation for phase space reconstruction with code optimization strategies
This article presents a MATLAB-based implementation of using autocorrelation functions to determine delay time for chaotic time series, including algorithmic approaches and key programming considerations for practical applications.
Algorithm for calculating optimal delay time in chaotic time series analysis, featuring implementation approaches using autocorrelation and mutual information methods for phase space reconstruction
MATLAB implementation for computing mutual information to determine optimal delay time t in chaos analysis, featuring state discretization and parameter optimization algorithms
Original MATLAB program implementing the DelayTime_MutualInformation method to calculate optimal delay time for chaotic time series analysis
Implementation of CC Algorithm for Determining Optimal Delay Time and Embedding Dimension in Time Series Phase Space Reconstruction
Implementation of GP Algorithm to Determine Optimal Embedding Parameters (Dimension and Delay Time) for Time Series Phase Space Reconstruction
Optimizing delay time in traffic light design at intersections using a fuzzy logic control system. This system regulates green light duration with two input variables: average vehicle speed during green phase and average speed during red phase, and one output variable: adjusted green light time. The implementation uses five membership functions (slowest, slow, normal, fast, fastest) for granular control. Key algorithmic components include fuzzification, rule-based inference, and defuzzification processes to dynamically balance traffic flow efficiency.