Adaptive Learning Methods for Nonlinear System Modeling

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 always entail a certain degree of nonlinearity, which makes linear models a non-optimal choice. This book mainly focuses on those methodologies for nonlinear modeling that involve any adaptive learning approaches to process data coming from an unknown nonlinear system. By learning from available data, such methods aim at estimating the nonlinearity introduced by the unknown system. In particular, the methods presented in this book are based on online learning approaches, which process the data example-by-example and allow to model even complex nonlinearities, e.g., showing time-varying and dynamic behaviors. Possible fields of applications of such algorithms includes distributed sensor networks, wireless communications, channel identification, predictive maintenance, wind prediction, network security, vehicular networks, active noise control, information forensics and security, tracking control in mobile robots, power systems, and nonlinear modeling in big data, among many others. This book serves as a crucial resource for researchers, PhD and post-graduate students working in the areas of machine learning, signal processing, adaptive filtering, nonlinear control, system identification, cooperative systems, computational intelligence. This book may be also of interest to the industry market and practitioners working with a wide variety of nonlinear systems. Presents the key trends and future perspectives in the field of nonlinear signal processing and adaptive learning. Introduces novel solutions and improvements over the state-of-the-art methods in the very exciting area of online and adaptive nonlinear identification. Helps readers understand important methods that are effective in nonlinear system modelling, suggesting the right methodology to address particular issues.

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  • Author : Danilo Comminiello
  • Publisher : Butterworth-Heinemann
  • Pages : 388 pages
  • ISBN : 0128129778
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKAdaptive Learning Methods for Nonlinear System Modeling

Adaptive Learning Methods for Nonlinear System Modeling

Adaptive Learning Methods for Nonlinear System Modeling
  • Author : Danilo Comminiello,Jose C. Principe
  • Publisher : Butterworth-Heinemann
  • Release : 11 June 2018
GET THIS BOOKAdaptive Learning Methods for Nonlinear System Modeling

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 always entail a certain degree of nonlinearity, which makes linear models a non-optimal choice. This book mainly focuses on those methodologies for nonlinear modeling that involve any adaptive learning approaches to process data coming from an unknown nonlinear system. By learning from available data, such methods aim at estimating the nonlinearity

Nonlinear System Identification

Nonlinear System Identification
  • Author : Oliver Nelles
  • Publisher : Springer Science & Business Media
  • Release : 14 April 2021
GET THIS BOOKNonlinear System Identification

Written from an engineering point of view, this book covers the most common and important approaches for the identification of nonlinear static and dynamic systems. The book also provides the reader with the necessary background on optimization techniques, making it fully self-contained. The new edition includes exercises.

Learning-Based Adaptive Control

Learning-Based Adaptive Control
  • Author : Mouhacine Benosman
  • Publisher : Butterworth-Heinemann
  • Release : 02 August 2016
GET THIS BOOKLearning-Based Adaptive Control

Adaptive control has been one of the main problems studied in control theory. The subject is well understood, yet it has a very active research frontier. This book focuses on a specific subclass of adaptive control, namely, learning-based adaptive control. As systems evolve during time or are exposed to unstructured environments, it is expected that some of their characteristics may change. This book offers a new perspective about how to deal with these variations. By merging together Model-Free and Model-Based

Self-Learning Optimal Control of Nonlinear Systems

Self-Learning Optimal Control of Nonlinear Systems
  • Author : Qinglai Wei,Ruizhuo Song,Benkai Li,Xiaofeng Lin
  • Publisher : Springer
  • Release : 13 June 2017
GET THIS BOOKSelf-Learning Optimal Control of Nonlinear Systems

This book presents a class of novel, self-learning, optimal control schemes based on adaptive dynamic programming techniques, which quantitatively obtain the optimal control schemes of the systems. It analyzes the properties identified by the programming methods, including the convergence of the iterative value functions and the stability of the system under iterative control laws, helping to guarantee the effectiveness of the methods developed. When the system model is known, self-learning optimal control is designed on the basis of the system

Adaptive Learning of Polynomial Networks

Adaptive Learning of Polynomial Networks
  • Author : Nikolay Nikolaev,Hitoshi Iba
  • Publisher : Springer Science & Business Media
  • Release : 18 August 2006
GET THIS BOOKAdaptive Learning of Polynomial Networks

This book delivers theoretical and practical knowledge for developing algorithms that infer linear and non-linear multivariate models, providing a methodology for inductive learning of polynomial neural network models (PNN) from data. The text emphasizes an organized model identification process by which to discover models that generalize and predict well. The book further facilitates the discovery of polynomial models for time-series prediction.

Adaptive Nonlinear System Identification

Adaptive Nonlinear System Identification
  • Author : Tokunbo Ogunfunmi
  • Publisher : Springer Science & Business Media
  • Release : 05 September 2007
GET THIS BOOKAdaptive Nonlinear System Identification

Focuses on System Identification applications of the adaptive methods presented. but which can also be applied to other applications of adaptive nonlinear processes. Covers recent research results in the area of adaptive nonlinear system identification from the authors and other researchers in the field.

Block-oriented Nonlinear System Identification

Block-oriented Nonlinear System Identification
  • Author : Fouad Giri,Er-Wei Bai
  • Publisher : Springer Science & Business Media
  • Release : 18 August 2010
GET THIS BOOKBlock-oriented Nonlinear System Identification

Block-oriented Nonlinear System Identification deals with an area of research that has been very active since the turn of the millennium. The book makes a pedagogical and cohesive presentation of the methods developed in that time. These include: iterative and over-parameterization techniques; stochastic and frequency approaches; support-vector-machine, subspace, and separable-least-squares methods; blind identification method; bounded-error method; and decoupling inputs approach. The identification methods are presented by authors who have either invented them or contributed significantly to their development. All the