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Author: Han-Xiong Li
Publisher: Springer Science & Business Media
ISBN: 940070741X
Category : Mathematics
Languages : en
Pages : 175
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Book Description
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.
Author: Han-Xiong Li
Publisher: Springer Science & Business Media
ISBN: 940070741X
Category : Mathematics
Languages : en
Pages : 175
Get Book Here
Book Description
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.
Author: Janya-anurak, Chettapong
Publisher: KIT Scientific Publishing
ISBN: 3731506424
Category : Electronic computers. Computer science
Languages : en
Pages : 248
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Book Description
In this work, the Uncertainty Quantification (UQ) approaches combined systematically to analyze and identify systems. The generalized Polynomial Chaos (gPC) expansion is applied to reduce the computational effort. The framework using gPC based on Bayesian UQ proposed in this work is capable of analyzing the system systematically and reducing the disagreement between the model predictions and the measurements of the real processes to fulfill user defined performance criteria.
Author: Danilo Comminiello
Publisher: Butterworth-Heinemann
ISBN: 0128129778
Category : Technology & Engineering
Languages : en
Pages : 390
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Book Description
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.
Author: Jili Tao
Publisher: Springer Nature
ISBN: 981155403X
Category : Computers
Languages : en
Pages : 280
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Book Description
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.
Author: Simona Vlad
Publisher: Springer
ISBN: 9811362076
Category : Technology & Engineering
Languages : en
Pages : 330
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Book Description
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
Author: Han-Xiong Li
Publisher: John Wiley & Sons
ISBN: 1118822331
Category : Technology & Engineering
Languages : en
Pages : 264
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Book Description
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.
Author: Oleg Gusikhin
Publisher: Springer Nature
ISBN: 3030319938
Category : Technology & Engineering
Languages : en
Pages : 587
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Book Description
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.
Author: Yizhi Wang
Publisher: Elsevier
ISBN: 0323953913
Category : Mathematics
Languages : en
Pages : 318
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Book Description
Modeling and Control of Dynamic Spatially Distributed Systems: Pharmaceutical Processes provides a balanced approach to help readers to get started quickly in the field of biochemical pharmaceuticals. From a theoretical perspective, dynamic spatially distributed systems are introduced to address their industrial applications. After identifying problems, the book provides readers with modeling and control system design techniques via a novel fuzzy set (class of objects with a continuum of grades of membership, to describe the grade of the object belonging to this fuzzy set) and intelligent computation methods. From an application perspective, the book provides a thorough understanding of Good Manufacture Practices (GMP) and the importance of identification, modelling, and intelligent control of such systems, reducing the test-and-error cost, and the R&D design time cycle of original drug development. - Provides an updated, supplemental knowledge to the body of distributed parameter systems - Covers control and analysis framework based on a state-space approach for a non-standard model from industrial complex systems - Presents a novel proposed fuzzy set and applies it to case studies to illustrate its feasibility - Includes a control system design solution from perspective of medicine production
Author: Bor-Sen Chen
Publisher: CRC Press
ISBN: 1000514064
Category : Mathematics
Languages : en
Pages : 459
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Book Description
Most systems in science, engineering, and biology are of partial differential systems (PDSs) modeled by partial differential equations. Many books about partial differential equations have been written by mathematicians and mainly address some fundamental mathematic backgrounds and discuss some mathematic properties of partial differential equations. Only a few books on PDSs have been written by engineers; however, these books have focused mainly on the theoretical stabilization analysis of PDSs, especially mechanical systems. This book investigates both robust stabilization control design and robust filter design and reference tracking control design in mechanical, signal processing, and control systems to fill a gap in the study of PDSs. Robust Engineering Designs of Partial Differential Systems and Their Applications offers some fundamental background in the first two chapters. The rest of the chapters focus on a specific design topic with a corresponding deep investigation into robust H∞ filtering, stabilization, or tracking design for more complex and practical PDSs under stochastic fluctuation and external disturbance. This book is aimed at engineers and scientists and addresses the gap between the theoretical stabilization results of PDSs in academic and practical engineering designs more focused on the robust H∞ filtering, stabilization, and tracking control problems of linear and nonlinear PDSs under intrinsic random fluctuation and external disturbance in industrial applications. Part I provides backgrounds on PDSs, such as Galerkin’s, and finite difference methods to approximate PDSs and a fuzzy method to approximate nonlinear PDSs. Part II examines robust H∞ filter designs for the robust state estimation of linear and nonlinear stochastic PDSs. And Part III treats robust H∞ stabilization and tracking control designs of linear and nonlinear PDSs. Every chapter focuses on an engineering design topic with both theoretical design analysis and practical design examples.
Author: Simona Vlad
Publisher: Springer
ISBN: 3319076531
Category : Technology & Engineering
Languages : en
Pages : 348
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Book Description
This volume presents the contributions of the third International Conference on Advancements of Medicine and Health Care through Technology (Meditech 2014), held in in Cluj-Napoka, 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.
