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

The First Outstanding 50 Years of “Università Politecnica delle Marche”

The First Outstanding 50 Years of “Università Politecnica delle Marche”
  • Author : Sauro Longhi,Andrea Monteriù,Alessandro Freddi,Emanuele Frontoni,Michele Germani,Gian Marco Revel
  • Publisher : Springer Nature
  • Release : 16 December 2019
GET THIS BOOKThe First Outstanding 50 Years of “Università Politecnica delle Marche”

The book describes the significant multidisciplinary research findings at the Università Politecnica delle Marche and the expected future advances. It addresses some of the most dramatic challenges posed by today’s fast-growing, global society and the changes it has caused. It also discusses solutions to improve the wellbeing of human beings. The book covers the main research achievements in the different disciplines of the physical sciences and engineering, as well as several research lines developed at the university’s Faculty

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.

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.

Intelligent Industrial Systems: Modeling, Automation and Adaptive Behavior

Intelligent Industrial Systems: Modeling, Automation and Adaptive Behavior
  • Author : Rigatos, Gerasimos
  • Publisher : IGI Global
  • Release : 30 June 2010
GET THIS BOOKIntelligent Industrial Systems: Modeling, Automation and Adaptive Behavior

In recent years, there has been growing interest in industrial systems, especially in robotic manipulators and mobile robot systems. As the cost of robots goes down and become more compact, the number of industrial applications of robotic systems increases. Moreover, there is need to design industrial systems with intelligence, autonomous decision making capabilities, and self-diagnosing properties. Intelligent Industrial Systems: Modeling, Automation and Adaptive Behavior analyzes current trends in industrial systems design, such as intelligent, industrial, and mobile robotics, complex electromechanical

Nonlinear System Identification

Nonlinear System Identification
  • Author : Oliver Nelles
  • Publisher : Springer Science & Business Media
  • Release : 15 January 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.

Life System Modeling and Intelligent Computing

Life System Modeling and Intelligent Computing
  • Author : Kang Li,Xin Li,Shiwei Ma,George W. Irwin
  • Publisher : Springer Science & Business Media
  • Release : 03 September 2010
GET THIS BOOKLife System Modeling and Intelligent Computing

The 2010 International Conference on Life System Modeling and Simulation (LSMS 2010) and the 2010 International Conference on Intelligent Computing for Susta- able Energy and Environment (ICSEE 2010) were formed to bring together resear- ers and practitioners in the fields of life system modeling/simulation and intelligent computing applied to worldwide sustainable energy and environmental applications. A life system is a broad concept, covering both micro and macro components ra- ing from cells, tissues and organs across to organisms and ecological niches. To c-

Artificial Neural Networks for Modelling and Control of Non-Linear Systems

Artificial Neural Networks for Modelling and Control of Non-Linear Systems
  • Author : Johan A.K. Suykens,Joos P.L. Vandewalle,B.L. de Moor
  • Publisher : Springer Science & Business Media
  • Release : 31 December 1995
GET THIS BOOKArtificial Neural Networks for Modelling and Control of Non-Linear Systems

Artificial neural networks possess several properties that make them particularly attractive for applications to modelling and control of complex non-linear systems. Among these properties are their universal approximation ability, their parallel network structure and the availability of on- and off-line learning methods for the interconnection weights. However, dynamic models that contain neural network architectures might be highly non-linear and difficult to analyse as a result. Artificial Neural Networks for Modelling and Control of Non-Linear Systems investigates the subject from a

Advances in Neural Networks - ISNN 2007

Advances in Neural Networks - ISNN 2007
  • Author : Derong Liu,Shumin Fei,Zeng-Guang Hou,Changyin Sun,Huaguang Zhang
  • Publisher : Springer Science & Business Media
  • Release : 24 May 2007
GET THIS BOOKAdvances in Neural Networks - ISNN 2007

The three volume set LNCS 4491/4492/4493 constitutes the refereed proceedings of the 4th International Symposium on Neural Networks, ISNN 2007, held in Nanjing, China in June 2007. The 262 revised long papers and 192 revised short papers presented were carefully reviewed and selected from a total of 1.975 submissions. The papers are organized in topical sections on neural fuzzy control, neural networks for control applications, adaptive dynamic programming and reinforcement learning, neural networks for nonlinear systems modeling, robotics, stability analysis of neural networks, learning and approximation,

Principles of Adaptive Filters and Self-learning Systems

Principles of Adaptive Filters and Self-learning Systems
  • Author : Anthony Zaknich
  • Publisher : Springer Science & Business Media
  • Release : 30 March 2006
GET THIS BOOKPrinciples of Adaptive Filters and Self-learning Systems

Teaches students about classical and nonclassical adaptive systems within one pair of covers Helps tutors with time-saving course plans, ready-made practical assignments and examination guidance The recently developed "practical sub-space adaptive filter" allows the reader to combine any set of classical and/or non-classical adaptive systems to form a powerful technology for solving complex nonlinear problems

Model Free Adaptive Control

Model Free Adaptive Control
  • Author : Zhongsheng Hou,Shangtai Jin
  • Publisher : CRC Press
  • Release : 24 September 2013
GET THIS BOOKModel Free Adaptive Control

Model Free Adaptive Control: Theory and Applications summarizes theory and applications of model-free adaptive control (MFAC). MFAC is a novel adaptive control method for the unknown discrete-time nonlinear systems with time-varying parameters and time-varying structure, and the design and analysis of MFAC merely depend on the measured input and output data of the controlled plant, which makes it more applicable for many practical plants. This book covers new concepts, including pseudo partial derivative, pseudo gradient, pseudo Jacobian matrix, and generalized

Neuro-Fuzzy Control of Industrial Systems with Actuator Nonlinearities

Neuro-Fuzzy Control of Industrial Systems with Actuator Nonlinearities
  • Author : Frank L. Lewis,Javier Campos,Rastko Selmic
  • Publisher : SIAM
  • Release : 01 January 2002
GET THIS BOOKNeuro-Fuzzy Control of Industrial Systems with Actuator Nonlinearities

Rigorous stability proofs are further verified by computer simulations, and appendices contain the computer code needed to build intelligent controllers for real-time applications. Neural networks capture the parallel processing and learning capabilities of biological nervous systems, and fuzzy logic captures the decision-making capabilities of human linguistics and cognitive systems.