• Home
  • Adaptive Learning Methods for Nonlinear System Modeling

Adaptive Learning Methods for Nonlinear System Modeling

Adaptive Learning Methods for Nonlinear System Modeling
  • Author : Danilo Comminiello
  • Publsiher : Butterworth-Heinemann
  • Release : 11 June 2018
  • ISBN : 0128129778
  • Pages : 388 pages
  • Rating : 4/5 from 21 ratings
GET THIS BOOKAdaptive Learning Methods for Nonlinear System Modeling

Summary:
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

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


Nonlinear System Identification

Nonlinear System Identification
  • Author : Oliver Nelles
  • Publisher : Springer Science & Business Media
  • Release : 01 March 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.


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.



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




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.



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-


1999 IEEE/ASME International Conference on Advanced Intelligent Mechatronics Proceedings

1999 IEEE/ASME International Conference on Advanced Intelligent Mechatronics Proceedings
  • Author : IEEE Industrial Electronics Society
  • Publisher : Institute of Electrical & Electronics Engineers(IEEE)
  • Release : 01 March 1999
GET THIS BOOK1999 IEEE/ASME International Conference on Advanced Intelligent Mechatronics Proceedings

Topics in these conference papers include: micro/nano manipulation; bioengineering;cooperative robots; high-tech actuators; automotive technology; grasping and haptics; and mechanical redundancy.


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


Soft Computing

Soft Computing
  • Author : Fred Aminzadeh,Mohammad Jamshidi
  • Publisher : Prentice Hall
  • Release : 01 March 1994
GET THIS BOOKSoft Computing

This volume presents a collection of articles on state-of-the-art soft computing and AI applications that cover broad domains and many disciplines. The authors explain the evolution of the mathematics behind the intelligent systems; consider fuzzy logic and neural network applications; and explore several AI applications.