Adaptive Learning Methods For Nonlinear System Modeling

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Adaptive Learning Methods For Nonlinear System Modeling

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Author : Danilo Comminiello
language : en
Publisher: Butterworth-Heinemann
Release Date : 2018-06-11

Adaptive Learning Methods For Nonlinear System Modeling written by Danilo Comminiello and has been published by Butterworth-Heinemann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-11 with Technology & Engineering categories.

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.

Adaptive Learning Methods For Nonlinear System Modeling

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Author : Danilo Comminiello
language : en
Publisher: Butterworth-Heinemann
Release Date : 2018-07-05

Adaptive Learning Methods For Nonlinear System Modeling written by Danilo Comminiello and has been published by Butterworth-Heinemann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-05 with Technology & Engineering categories.

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.

Adaptive Nonlinear System Identification

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Author : Tokunbo Ogunfunmi
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-09-05

Adaptive Nonlinear System Identification written by Tokunbo Ogunfunmi and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-09-05 with Science categories.

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.

Nonlinear And Adaptive Control With Applications

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Author : Alessandro Astolfi
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-12-06

Nonlinear And Adaptive Control With Applications written by Alessandro Astolfi and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-12-06 with Technology & Engineering categories.

The authors here provide a detailed treatment of the design of robust adaptive controllers for nonlinear systems with uncertainties. They employ a new tool based on the ideas of system immersion and manifold invariance. New algorithms are delivered for the construction of robust asymptotically-stabilizing and adaptive control laws for nonlinear systems. The methods proposed lead to modular schemes that are easier to tune than their counterparts obtained from Lyapunov redesign.

Adaptive Learning Of Polynomial Networks

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Author : Nikolay Nikolaev
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-08-18

Adaptive Learning Of Polynomial Networks written by Nikolay Nikolaev and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-08-18 with Computers categories.

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.

Concept Of Adaptive Filtering

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Author : Shivendra Nandan
language : en
Publisher: TSG Publications
Release Date :

Concept Of Adaptive Filtering written by Shivendra Nandan and has been published by TSG Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on with Education categories.

A spline adaptive filter (SAF) based nonlinear active noise control (ANC) system is proposed in this paper. The SAF consists of a linear network of adaptive weights in a cascade with an adaptive nonlinear network. The nonlinear network, in turn consists of an adaptive look-up table followed by a spline interpolation network and forms an adaptive activation function. An update rule has been derived for the proposed ANC system, which not only updates the weights of the linear network, but also updates the nature of the activation function. Linear Network is based on improvement in FxLMS algorithm. FxLMS algorithm is used because it is computationally simple like the most commonly used Least Mean Square (LMS) algorithm. In addition, it includes secondary path effects. To make the FxLMS algorithm more effective, the secondary path estimation should be more precise and accurate. The nonlinear function involved in the adaptation process is based on a spline function that can be modified during learning. The spline control points are adaptively changed using gradient-based techniques. B-splines and Catmull-Rom splines are used, because they allow imposing simple constraints on control parameters. This new kind of adaptive function is then applied to the output of a linear adaptive filter and it is used for the identification of Wiener-type nonlinear systems. In addition, we derive a simple form of the adaptation algorithm and an upper bound on the choice of the step-size. An extensive simulation study has been conducted to evaluate the noise mitigation performance of the proposed scheme and the new method has been shown to provide improved noise cancellation efficiency with a lesser computational load in comparison with other popular ANC systems.

Sequential Intelligent Dynamic System Modeling And Control

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Author : Hai-Jun Rong
language : en
Publisher: Springer Nature
Release Date : 2024-07-05

Sequential Intelligent Dynamic System Modeling And Control written by Hai-Jun Rong and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-05 with Technology & Engineering categories.

The book offers novel research results of sequential intelligent dynamic system modeling and control in a unified framework from theory proposals to real applications. It covers an in-depth study of various learning algorithms for the permanent adaptation of intelligent model parameters as well as of structural parts of the model. The comprehensive researches on sequential fuzzy and neural controller design schemes for some complex real applications are included. This is particularly suited for readers who are interested to learn practical solutions for controlling nonlinear systems that are uncertain and varied at any time. In addition, the organization of the book from addressing fundamental concepts, and presenting novel intelligent models to solving real applications is one of the major features of the book, which makes it a valuable resource for both beginners and researchers wanting to further their understanding and study about realtime online intelligent modeling and control ofnonlinear dynamic systems. The book can benefit researchers, engineers, and graduate students in the fields of control engineering, artificial intelligence, computational intelligence, intelligent control, nonlinear system modeling, and control, etc.

High Level Feedback Control With Neural Networks

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Author : Young Ho Kim
language : en
Publisher: World Scientific
Release Date : 1998-09-28

High Level Feedback Control With Neural Networks written by Young Ho Kim and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998-09-28 with Technology & Engineering categories.

Complex industrial or robotic systems with uncertainty and disturbances are difficult to control. As system uncertainty or performance requirements increase, it becomes necessary to augment traditional feedback controllers with additional feedback loops that effectively “add intelligence” to the system. Some theories of artificial intelligence (AI) are now showing how complex machine systems should mimic human cognitive and biological processes to improve their capabilities for dealing with uncertainty.This book bridges the gap between feedback control and AI. It provides design techniques for “high-level” neural-network feedback-control topologies that contain servo-level feedback-control loops as well as AI decision and training at the higher levels. Several advanced feedback topologies containing neural networks are presented, including “dynamic output feedback”, “reinforcement learning” and “optimal design”, as well as a “fuzzy-logic reinforcement” controller. The control topologies are intuitive, yet are derived using sound mathematical principles where proofs of stability are given so that closed-loop performance can be relied upon in using these control systems. Computer-simulation examples are given to illustrate the performance.

Applied Mechanics Reviews

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Author :
language : en
Publisher:
Release Date : 1992

Applied Mechanics Reviews written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with Mechanics, Applied categories.

Advances In Artificial Intelligence Iberamia 2004

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Author : Christian Lemaitre
language : en
Publisher: Springer
Release Date : 2004-11-03

Advances In Artificial Intelligence Iberamia 2004 written by Christian Lemaitre and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-11-03 with Computers categories.

This book constitutes the refereed proceedings of the 9th Ibero-American Conference on Artificial Intelligence, IBERAMIA 2004, held in Puebla, Mexico in November 2004. The 97 revised full papers presented were carefully reviewed and selected from 304 submissions. The papers are organized in topical sections on distributed AI and multi-agent systems, knowledge engineering and case-based reasoning, planning and scheduling, machine learning and knowledge acquisition, natural language processing, knowledge representation and reasoning, knowledge discovery and data mining, robotics, computer vision, uncertainty and fuzzy systems, genetic algorithms and neural networks, AI in education, and miscellaneous topics.