Adaptive Learning Methods for Nonlinear System Modeling

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Adaptive Learning Methods for Nonlinear System Modeling - Danilo Comminiello (Editor), Jose C. Principe (Editor) 2018 PDF Elsevier Inc. BOOKS PROGRAMMING

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Adaptive Learning Methods for Nonlinear System Modeling

Author: Danilo Comminiello (Editor), Jose C. Principe (Editor)
Year: 2018
Format: PDF
File size: 10.8 MB
Language: ENG

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The book also covers topics such as parameter estimation, convergence analysis, stability analysis, and computational complexity. Book Description: Adaptive Learning Methods for Nonlinear System Modeling Authors: [insert author names] Publication Date: [insert publication date] Pages: [insert page count] Publisher: [insert publisher name] Genre: Science, Technology, Engineering, Mathematics (STEM) Adaptive Learning Methods for Nonlinear System Modeling presents some of the recent advances on adaptive algorithms and machine learning methods designed for nonlinear system modeling and identification. Real-life problems often entail a certain degree of nonlinearity, making linear models suboptimal choices. This book primarily focuses on methodologies for nonlinear modeling that involve adaptive learning approaches to process data from an unknown nonlinear system and learn its underlying dynamics. Topics covered in the book include parameter estimation, convergence analysis, stability analysis, and computational complexity. The need to study and understand the technological evolution process is crucial for the survival of humanity and the unity of people in a warring state. As technology continues to advance at an unprecedented rate, it is imperative to develop a personal paradigm for perceiving the technological process of developing modern knowledge. This paradigm will enable us to better understand the rapid changes in technology and adapt to them effectively.

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