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About This Product
(This product is a product of IPA specific program development and dissemination project)
Chaotic complex systems analysis package. With waveform editing/preprocessing function. Chaos is a complex phenomenon resulting from simple nonlinear laws. Chaos analysis allows us to discover underlying laws from complex data, grasp delicate and deep features that cannot be obtained through spectral analysis or statistical analysis based on conventional linear theory, and predict behavior patterns. Example: Used to predict falling asleep while driving.
■Overview of chaos analysis program
Time-series data generated by a constructive simulation program, biological signals such as brain waves, pulse waves, body temperature, electrocardiogram, blood pressure, and voice data, mechanical vibrations of motors, cars, tractors, etc., electric power usage, and water usage. All kinds of time-series data collected from observation systems, such as water volume, changes in dam water levels, internet line usage rates, stock price fluctuations, temperature changes, population changes, etc., are embedded into attractors on the state space using chaos methods, and their geometry is complexity of shapes (fractal dimension) The degree of chaos can be quantified by calculating the orbital instability (Lyapunov spectrum).
In addition, it has extensive time-series data processing and filtering functions required for pre-calculation processing, as well as the ability to generate basic chaotic data (Lorentzian, Wrestler, and Logistic).
■Function list
・Number of data points to be analyzed: Supports up to 100,000 sampling points
・Analysis supported dimensions: Supports up to 20 dimensions
Editing function
■Smoothing
Smooth out fluctuations in time series data using a moving average.
■Interpolation
Increase the number of observation (measurement) points for time series data with a small number of points.
■ Thinning out
Reduce the number of time-series data with many observation (measurement) points.
■Zero adjustment
Set the intermediate value of the data to 0.
■Scaling
Enter the upper and lower limits and normalize the data within those limits.
■Noise cut
Remove noise contained in time series waveforms.
■R-R interval
The interval between the maximum value points of the detected waveform is replaced with time series data.
■Envelope
Spline interpolation is performed on the maximum value of the detected waveform and replaced with time series data.
■Filter
Replace with time series data excluding high, medium, and low frequency components.
Operating environment
■CPU
A personal computer equipped with a Pentium III 266MHz or higher and running Microsoft Windows 2000/XP/Vista/7
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Product
Chaotic complex system simulation system
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