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

XIV International Scientific Conference “INTERAGROMASH 2021"

XIV International Scientific Conference “INTERAGROMASH 2021
  • Author : Alexey Beskopylny,Mark Shamtsyan
  • Publisher : Springer Nature
  • Release : 01 December 2021
GET THIS BOOKXIV International Scientific Conference “INTERAGROMASH 2021"

This book contains original and fundamental research papers in the following areas: engineering technologies for precision agriculture, agricultural systems management and digitalization in agriculture, logistics in agriculture, and other topics. Selected materials of the largest regional scientific event—INTERAGROMASH 2021 conference–included in this book present the results of the latest research in the areas of precision agriculture and agricultural machinery industry. The book is aimed for professionals and practitioners, for researchers, scholars, and producers. The materials presented here are used

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Latest Trends in Renewable Energy Technologies
  • Author : Shelly Vadhera,Bhimrao S. Umre,Akhtar Kalam
  • Publisher : Springer Nature
  • Release : 28 November 2021
GET THIS BOOKLatest Trends in Renewable Energy Technologies

This book presents select proceedings of the National Conference on Renewable Energy and Sustainable Environment (NCRESE 2020) and examines a range of reliable energy-efficient harvesting technologies, their applications and utilization of available alternate energy resources. The topics covered include alternate energy technologies, smart grid topologies and their relevant issues, solar thermal and bio-energy systems, electric vehicles and energy storage systems and its control issues. The book also discusses various properties and performance attributes of advance renewable energy techniques and impact on

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Adaptive Critic Control with Robust Stabilization for Uncertain Nonlinear Systems
  • Author : Ding Wang,Chaoxu Mu
  • Publisher : Springer
  • Release : 10 August 2018
GET THIS BOOKAdaptive Critic Control with Robust Stabilization for Uncertain Nonlinear Systems

This book reports on the latest advances in adaptive critic control with robust stabilization for uncertain nonlinear systems. Covering the core theory, novel methods, and a number of typical industrial applications related to the robust adaptive critic control field, it develops a comprehensive framework of robust adaptive strategies, including theoretical analysis, algorithm design, simulation verification, and experimental results. As such, it is of interest to university researchers, graduate students, and engineers in the fields of automation, computer science, and electrical

Lagrangian and Hamiltonian Methods for Nonlinear Control 2003

Lagrangian and Hamiltonian Methods for Nonlinear Control 2003
  • Author : A Astolfi,Francisco Gordillo,A J Van Der Schaft
  • Publisher : Elsevier
  • Release : 21 October 2003
GET THIS BOOKLagrangian and Hamiltonian Methods for Nonlinear Control 2003

This is the second of a series of IFAC Workshops initiated in 2000. The first one chaired and organized by Profs. N. Leonard and R. Ortega, was held in Princeton in March 2000. This proceedings volume looks at the role-played by Lagrangian and Hamiltonian methods in disciplines such as classical mechanics, quantum mechanics, fluid dynamics, electrodynamics, celestial mechanics and how such methods can be practically applied in the control community. *Presents and illustrates new approaches to nonlinear control that exploit the Lagrangian

Intelligent Adaptive Control

Intelligent Adaptive Control
  • Author : Lakhmi C. Jain,Clarence W. de Silva
  • Publisher : CRC Press
  • Release : 29 December 1998
GET THIS BOOKIntelligent Adaptive Control

This book describes important techniques, developments, and applications of computational intelligence in system control. Chapters present: an introduction to the fundamentals of neural networks, fuzzy logic, and evolutionary computing a rigorous treatment of intelligent control industrial applications of intelligent control and soft computing, including transportation, petroleum, motor drive, industrial automation, and fish processing other knowledge-based techniques, including vehicle driving aid and air traffic management Intelligent Adaptive Control provides a state-of-the-art treatment of practical applications of computational intelligence in system control.

Intelligent Systems

Intelligent Systems
  • Author : Cornelius T. Leondes
  • Publisher : CRC Press
  • Release : 08 October 2018
GET THIS BOOKIntelligent Systems

Intelligent systems, or artificial intelligence technologies, are playing an increasing role in areas ranging from medicine to the major manufacturing industries to financial markets. The consequences of flawed artificial intelligence systems are equally wide ranging and can be seen, for example, in the programmed trading-driven stock market crash of October 19, 1987. Intelligent Systems: Technology and Applications, Six Volume Set connects theory with proven practical applications to provide broad, multidisciplinary coverage in a single resource. In these volumes, international experts present case-study

Algorithms and Architectures for Real-Time Control 1991

Algorithms and Architectures for Real-Time Control 1991
  • Author : P.J. Fleming,D.I. Jones
  • Publisher : Elsevier
  • Release : 22 July 2014
GET THIS BOOKAlgorithms and Architectures for Real-Time Control 1991

Computer scientists have long appreciated that the relationship between algorithms and architecture is crucial. Broadly speaking the more specialized the architecture is to a particular algorithm then the more efficient will be the computation. The penalty is that the architecture will become useless for computing anything other than that algorithm. This message holds for the algorithms used in real-time automatic control as much as any other field. These Proceedings will provide researchers in this field with a useful up-to-date reference

Artificial Neural Networks for Engineering Applications

Artificial Neural Networks for Engineering Applications
  • Author : Alma Y. Alanis,Nancy Arana-Daniel,Carlos Lopez-Franco
  • Publisher : Academic Press
  • Release : 07 February 2019
GET THIS BOOKArtificial Neural Networks for Engineering Applications

Artificial Neural Networks for Engineering Applications presents current trends for the solution of complex engineering problems that cannot be solved through conventional methods. The proposed methodologies can be applied to modeling, pattern recognition, classification, forecasting, estimation, and more. Readers will find different methodologies to solve various problems, including complex nonlinear systems, cellular computational networks, waste water treatment, attack detection on cyber-physical systems, control of UAVs, biomechanical and biomedical systems, time series forecasting, biofuels, and more. Besides the real-time implementations, the

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

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Multi-Resolution Methods for Modeling and Control of Dynamical Systems
  • Author : Puneet Singla,John L. Junkins
  • Publisher : CRC Press
  • Release : 01 August 2008
GET THIS BOOKMulti-Resolution Methods for Modeling and Control of Dynamical Systems

Unifying the most important methodology in this field, Multi-Resolution Methods for Modeling and Control of Dynamical Systems explores existing approximation methods as well as develops new ones for the approximate solution of large-scale dynamical system problems. It brings together a wide set of material from classical orthogonal function approximation, neural network input-output approximation, finite element methods for distributed parameter systems, and various approximation methods employed in adaptive control and learning theory. With sufficient rigor and generality, the book promotes a

Process Neural Networks

Process Neural Networks
  • Author : Xingui He,Shaohua Xu
  • Publisher : Springer Science & Business Media
  • Release : 05 July 2010
GET THIS BOOKProcess Neural Networks

For the first time, this book sets forth the concept and model for a process neural network. You’ll discover how a process neural network expands the mapping relationship between the input and output of traditional neural networks and greatly enhances the expression capability of artificial neural networks. Detailed illustrations help you visualize information processing flow and the mapping relationship between inputs and outputs.