Automatic Model Selection in Local Linear Model Trees (LOLIMOT) for Nonlinear System Identification of a Transport Delay Process

Nelles, Oliver; Hecker, Oliver; Isermann, Rolf

doi:10.1016/s1474-6670(17)42927-2

Cited by 15 publications

(3 citation statements)

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“…• A nonlinear dynamic model has been built for a truck Diesel engine turbocharger for a hardware-in-the-Ioop simulation [268,288,356]. • Concepts have been developed for nonlinear system identification and nonlinear predictive control of a tubular heat exchanger [144,281,283]. • Neural networks with internal and external dynamics are compared theoretically in [274].…”

Section: Discussionmentioning

confidence: 99%

“…A combination of a linear subset selection technique such as the orthogonalleast squares (OLS) algorithm with LOLIMOT proposed and applied in [270,281,283] allows one to partly solve the order determination problem by a structure optimization of the rule consequents. However, in addition to the static version presented in Sect.…”

Section: Structure Optimization Of the Rule Consequentsmentioning

confidence: 99%

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Nonlinear System Identification

Nelles

2001

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PrefaceThe goal of this book is to provide engineers and scientIsts in academia and industry with a thorough understanding of the underlying principles of nonlinear system identification. The reader will be able to apply the discussed models and methods to real problems with the necessary confidence and the awareness of potential difficulties that may arise in practice. This book is self-contained in the sense that it requires merely basic knowledge of matrix algebra, signals and systems, and statistics. Therefore, it also serves as an introduction to linear system identification and gives a practical overview on the major optimization methods used in engineering. The emphasis of this book is on an intuitive understanding of the subject and the practical application of the discussed techniques. It is not written in a theorem/proof style; rather the mathematics is kept to a minimum and the pursued ideas are illustrated by numerous figures, examples, and real-world applications.Fifteen years ago, nonlinear system identification was a field of several ad-hoc approaches, each applicable only to a very restricted class of systems. With the advent of neural networks, fuzzy models, and modern structure optimization techniques a much wider class of systems can be handled. Although one major characteristic of nonlinear systems is that almost every nonlinear system is unique, tools have been developed that allow the use of the same approach for a broad variety of systems. Certainly, a more problem-specific procedure typically promises superior performance, but from an industrial point of view a good tradeoff between development effort and performance is the decisive criterion for success. This book presents neural networks and fuzzy models together with major classical approaches in a unified framework. The strict distinction between the model architectures on the one hand (Part II) and the techniques for fitting these models to data on the other hand (Part I) tries to overcome the confusing mixture between both that is frequently encountered in the neuro and fuzzy literature. Nonlinear system identification is currently a field of very active research; many new methods will be developed and old methods will be refined. Nevertheless, I am confident that the underlying principles will continue to be valuable in the future.This book offers enough material for a two-semester course on optimization and nonlinear system identification. A higher level one-semester graduate course can focus on Chap. 7, the complete Part II, and Chaps. 17 to 21 in VI Preface Part III. For a more elementary one-semester course without prerequisites in optimization and linear system identification Chaps. 1 to 4 and 16 might be covered, while Chaps. 10, 14, 18, and 21 can be skipped. Alternatively, a course might omit the dynamic systems in Part III and instead emphasize the optimization techniques and nonlinear static modeling treated in Parts I and II. The applications presented in Part IV focus on a certain model architecture, and will convince th...

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Section: Discussionmentioning

confidence: 99%

Section: Structure Optimization Of the Rule Consequentsmentioning

confidence: 99%

Nonlinear System Identification

Nelles

2001

Self Cite

1,091

465

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“…The premise structure of an FNN, is determined by clustering the input space in [93] and identified by input-output pairs in [95]. A tree structure [85,100,103] and an iterative construction algorithm called Adaptive Spline Modelling of Observation Data (AS-MOD) [99] are developed for high-dimension partitioning and online-learning. In order to solve the slowness and trapping in local minima of Back Propagation (BP) algorithm [79,97], several paradigms with fast learning speed have been proposed , e.g., Resource Allocating Networks (RAN) [12] and its improved versions, such as the RANEKF [13] and the Minimal RAN (M-RAN) [14].…”

Section: Supervised Learningmentioning

confidence: 99%

Automatic generation of fuzzy neural networks via reinforcement learning with applications in path planning of mobile robots

Zhou¹

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Clustering and Q − value based Genetic Algorithm learning schemes f or F uzzy system design DF N N Dynamic F uzzy N eural N etwork DF QL Dynamic F uzzy Q − Learning DP Dynamic P rogramming DSGF QL Dynamic Self − Generated F uzzy Q − Learning DSGF QL − CA Dynamic Self − Generated F uzzy Q − Learning and Continuous Action Q − learning EBF Ellipsoidal Basis F unction EDSGF QL Enhanced Dynamic Self − Generated F uzzy Q − Learning F IS F uzzy Inf erence System F N N F uzzy N eural N etwork F QL F uzzy Q − Learning GA Genetic Algorithm GDF N N General Dynamic F uzzy N eural N etwork M F M embership F unction M OM M ean Of M aximum M OM O M ulti − Input M ulti − Output M OSI M ulti − Input Single − Output x

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Nonlinear System Identification with Neurofuzzy Methods

Nelles

1997

Intelligent Hybrid Systems

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Automatic Model Selection in Local Linear Model Trees (LOLIMOT) for Nonlinear System Identification of a Transport Delay Process

Cited by 15 publications

References 5 publications

Nonlinear System Identification

Nonlinear System Identification

Automatic generation of fuzzy neural networks via reinforcement learning with applications in path planning of mobile robots

Nonlinear System Identification with Neurofuzzy Methods

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