极值 Resources

Showing items tagged with "极值"

For unknown nonlinear functions, accurately finding extremum values solely through input-output data is challenging. This problem can be solved by combining neural networks with genetic algorithms, leveraging neural networks' nonlinear fitting capabilities and genetic algorithms' nonlinear optimization abilities to locate function extrema. This article demonstrates how to optimize extremum values for nonlinear functions using neural network genetic algorithms, with implementation details including network architecture design and genetic operation parameters.

MATLAB 337 views Tagged
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Application Background: Empirical Mode Decomposition (EMD) decomposes signals into monocomponent signals called Intrinsic Mode Functions (IMFs), enabling instantaneous frequency calculation through Hilbert transform. The primary challenge in practical Hilbert-Huang transform applications is the endpoint effect. Our solution introduces an adaptive spurious IMF filtering algorithm using residue-to-original-signal correlation coefficient as threshold. Key Technology: Complex signal decomposition into monocomponent signals requires each IMF to satisfy two conditions: (1) Extremum and zero-crossing counts must be equal or differ by one throughout the data length; (2) The mean of upper and lower envelopes must be zero at any point. The implementation involves adaptive sifting with envelope interpolation and statistical boundary handling.

MATLAB 351 views Tagged
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