An adaptive Gauss-Newton algorithm for training multilayer nonlinear filters that have embedded memory

DOI10.1007/BF01200791zbMath0938.93068OpenAlexW2003275068MaRDI QIDQ1971595

Yanyan Li

Publication date: 7 June 2000

Published in: Circuits, Systems, and Signal Processing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf01200791

zbMATH Keywords

neural networksGauss-Newton optimization traininginverse of a nonlinear dynamic tracking systemmultilayer nonlinear adaptive filters

Mathematics Subject Classification ID

Neural networks for/in biological studies, artificial life and related topics (92B20) Estimation and detection in stochastic control theory (93E10) Stochastic learning and adaptive control (93E35)

Related Items (1)

Comparison of the convergence rates of the new correntropy-based Levenberg-Marquardt (CLM) method and the fixed-point maximum correntropy (FP-MCC) algorithm

Cites Work

• Minimization of functions having Lipschitz continuous first partial derivatives
• Partial BFGS Update and Efficient Step-Length Calculation for Three-Layer Neural Networks
• Measuring Volterra kernels
• An Algorithm for Least-Squares Estimation of Nonlinear Parameters
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