Spatio Temporal Modeling Of Nonlinear Distributed Parameter Systems
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Spatio Temporal Modeling Of Nonlinear Distributed Parameter Systems
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Author : Han-Xiong Li
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-02-24
Spatio Temporal Modeling Of Nonlinear Distributed Parameter Systems written by Han-Xiong Li 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 2011-02-24 with Mathematics categories.
The purpose of this volume is to provide a brief review of the previous work on model reduction and identifi cation of distributed parameter systems (DPS), and develop new spatio-temporal models and their relevant identifi cation approaches. In this book, a systematic overview and classifi cation on the modeling of DPS is presented fi rst, which includes model reduction, parameter estimation and system identifi cation. Next, a class of block-oriented nonlinear systems in traditional lumped parameter systems (LPS) is extended to DPS, which results in the spatio-temporal Wiener and Hammerstein systems and their identifi cation methods. Then, the traditional Volterra model is extended to DPS, which results in the spatio-temporal Volterra model and its identification algorithm. All these methods are based on linear time/space separation. Sometimes, the nonlinear time/space separation can play a better role in modeling of very complex processes. Thus, a nonlinear time/space separation based neural modeling is also presented for a class of DPS with more complicated dynamics. Finally, all these modeling approaches are successfully applied to industrial thermal processes, including a catalytic rod, a packed-bed reactor and a snap curing oven. The work is presented giving a unifi ed view from time/space separation. The book also illustrates applications to thermal processes in the electronics packaging and chemical industry. This volume assumes a basic knowledge about distributed parameter systems, system modeling and identifi cation. It is intended for researchers, graduate students and engineers interested in distributed parameter systems, nonlinear systems, and process modeling and control.
Framework For Analysis And Identification Of Nonlinear Distributed Parameter Systems Using Bayesian Uncertainty Quantification Based On Generalized Polynomial Chaos
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Author : Janya-anurak, Chettapong
language : en
Publisher: KIT Scientific Publishing
Release Date : 2017-04-04
Framework For Analysis And Identification Of Nonlinear Distributed Parameter Systems Using Bayesian Uncertainty Quantification Based On Generalized Polynomial Chaos written by Janya-anurak, Chettapong and has been published by KIT Scientific Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-04-04 with categories.
Dna Computing Based Genetic Algorithm
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Author : Jili Tao
language : en
Publisher: Springer Nature
Release Date : 2020-07-01
Dna Computing Based Genetic Algorithm written by Jili Tao and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-01 with Computers categories.
This book focuses on the implementation, evaluation and application of DNA/RNA-based genetic algorithms in connection with neural network modeling, fuzzy control, the Q-learning algorithm and CNN deep learning classifier. It presents several DNA/RNA-based genetic algorithms and their modifications, which are tested using benchmarks, as well as detailed information on the implementation steps and program code. In addition to single-objective optimization, here genetic algorithms are also used to solve multi-objective optimization for neural network modeling, fuzzy control, model predictive control and PID control. In closing, new topics such as Q-learning and CNN are introduced. The book offers a valuable reference guide for researchers and designers in system modeling and control, and for senior undergraduate and graduate students at colleges and universities.
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.
6th International Conference On Advancements Of Medicine And Health Care Through Technology 17 20 October 2018 Cluj Napoca Romania
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Author : Simona Vlad
language : en
Publisher: Springer
Release Date : 2019-05-16
6th International Conference On Advancements Of Medicine And Health Care Through Technology 17 20 October 2018 Cluj Napoca Romania written by Simona Vlad and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-16 with Technology & Engineering categories.
This volume presents the contributions of the 6th International Conference on Advancements of Medicine and Health Care through Technology – MediTech 2018, held between 17 – 20 October 2018 in Cluj-Napoca, Romania. The papers of this Proceedings volume present new developments in : - Health Care Technology - Medical Devices, Measurement and Instrumentation - Medical Imaging, Image and Signal Processing - Modeling and Simulation - Molecular Bioengineering - Biomechanics
System Design And Control Integration For Advanced Manufacturing
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Author : Han-Xiong Li
language : en
Publisher: John Wiley & Sons
Release Date : 2014-10-27
System Design And Control Integration For Advanced Manufacturing written by Han-Xiong Li and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-10-27 with Technology & Engineering categories.
Most existing robust design books address design for static systems, or achieve robust design from experimental data via the Taguchi method. Little work considers model information for robust design particularly for the dynamic system. This book covers robust design for both static and dynamic systems using the nominal model information or the hybrid model/data information, and also integrates design with control under a large operating region. This design can handle strong nonlinearity and more uncertainties from model and parameters.
Informatics In Control Automation And Robotics
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Author : Oleg Gusikhin
language : en
Publisher: Springer Nature
Release Date : 2019-10-25
Informatics In Control Automation And Robotics written by Oleg Gusikhin and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-25 with Technology & Engineering categories.
The goal of this book is to familiarize readers with the latest research on, and recent advances in, the field of Informatics in Control, Automation and Robotics. It gathers a selection of papers highlighting the state-of-the-art in Intelligent Control Systems, Optimization, Robotics and Automation, Signal Processing, Sensors, Systems Modelling and Control. Combining theoretical aspects with practical applications, the book offers a well-balanced overview of the latest achievements, and will provide researchers, engineers and PhD students with both a vital update and new inspirations for their own research.
Nonlinear System Identification
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Author : Stephen A. Billings
language : en
Publisher: John Wiley & Sons
Release Date : 2013-09-23
Nonlinear System Identification written by Stephen A. Billings and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-09-23 with Technology & Engineering categories.
Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains describes a comprehensive framework for the identification and analysis of nonlinear dynamic systems in the time, frequency, and spatio-temporal domains. This book is written with an emphasis on making the algorithms accessible so that they can be applied and used in practice. Includes coverage of: The NARMAX (nonlinear autoregressive moving average with exogenous inputs) model The orthogonal least squares algorithm that allows models to be built term by term where the error reduction ratio reveals the percentage contribution of each model term Statistical and qualitative model validation methods that can be applied to any model class Generalised frequency response functions which provide significant insight into nonlinear behaviours A completely new class of filters that can move, split, spread, and focus energy The response spectrum map and the study of sub harmonic and severely nonlinear systems Algorithms that can track rapid time variation in both linear and nonlinear systems The important class of spatio-temporal systems that evolve over both space and time Many case study examples from modelling space weather, through identification of a model of the visual processing system of fruit flies, to tracking causality in EEG data are all included to demonstrate how easily the methods can be applied in practice and to show the insight that the algorithms reveal even for complex systems NARMAX algorithms provide a fundamentally different approach to nonlinear system identification and signal processing for nonlinear systems. NARMAX methods provide models that are transparent, which can easily be analysed, and which can be used to solve real problems. This book is intended for graduates, postgraduates and researchers in the sciences and engineering, and also for users from other fields who have collected data and who wish to identify models to help to understand the dynamics of their systems.
Advances In Machine Learning Research And Application 2012 Edition
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Author :
language : en
Publisher: ScholarlyEditions
Release Date : 2012-12-26
Advances In Machine Learning Research And Application 2012 Edition written by and has been published by ScholarlyEditions this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-26 with Computers categories.
Advances in Machine Learning Research and Application / 2012 Edition is a ScholarlyEditions™ eBook that delivers timely, authoritative, and comprehensive information about Machine Learning. The editors have built Advances in Machine Learning Research and Application / 2012 Edition on the vast information databases of ScholarlyNews.™ You can expect the information about Machine Learning in this eBook to be deeper than what you can access anywhere else, as well as consistently reliable, authoritative, informed, and relevant. The content of Advances in Machine Learning Research and Application / 2012 Edition has been produced by the world’s leading scientists, engineers, analysts, research institutions, and companies. All of the content is from peer-reviewed sources, and all of it is written, assembled, and edited by the editors at ScholarlyEditions™ and available exclusively from us. You now have a source you can cite with authority, confidence, and credibility. More information is available at http://www.ScholarlyEditions.com/.
Robust Engineering Designs Of Partial Differential Systems And Their Applications
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Author : Bor-Sen Chen
language : en
Publisher: CRC Press
Release Date : 2021-12-22
Robust Engineering Designs Of Partial Differential Systems And Their Applications written by Bor-Sen Chen and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-22 with Mathematics categories.
Considers both time‐domain and frequency domain robust design techniques of partial differential systems Illustrates both theoretical robust design techniques and practical applications Discusses partial differential systems with both Dirichlet and Neuman boundary conditions in robust design procedure Addresses deterministic and stochastic partial differential systems Explores theoretical mathematical background, robust signal processing design, robust control system design and robust biological system design with application