Automatische Strukturselektion für Fuzzy-Modelle zur Identifikation nichtlinearer, dynamischer Prozesse

Nelles, Oliver; Hecker, Oliver; Isermann, Rolf

doi:10.1524/auto.1998.46.6.302

Cited by 8 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

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465

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“…A priori knowledge can help to partition the input space manually. On the other hand, fuzzy-cluster algorithms or heuristic algorithms can be used for automatic partitioning (Höppner et al, 1997;Nelles et al, 1998). Suitable rules must be determined based on combinations of the fuzzy sets covering the entire input space of the application.…”

Section: Structure Identificationmentioning

confidence: 99%

Fuzzy logic-based rainfall—runoff modelling using soil moisture measurements to represent system state

Casper¹,

Gemmar

Gronz

et al. 2007

Hydrological Sciences Journal

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Initial catchment state, such as soil moisture, strongly controls rainfall-runoff transformation processes. However, due to the high spatial and temporal variability of soil moisture, point measurements may not always be suitable to represent the actual system state of a whole catchment as required in distributed catchment modelling. In this study a fuzzy rule-based system (FRBS) using the TakagiSugeno-Kang approach has been developed using soil moisture and rainfall as input variables to predict the actual discharge at the catchment outlet. Four soil moisture probes from the hydrological test site Dürreych (Black Forest, southwest Germany) were selected, each of them representing a particular runoff generation process (saturation excess flow, infiltration excess flow, slow and fast interflow, return flow). After manual calibration, the simulated peak discharges were very similar to the measured values. Furthermore, the pattern of rule activation in the FRBS reflected the complex, highly nonlinear behaviour of the catchment. Thus, in the FRBS framework, the measurements of soil moisture at representative locations could be used as representation for the actual system state, allowing for an entirely data-driven prediction of the runoff response using rainfall. . Après calage manuel, les pics de débit simulés sont très proches des valeurs mesurées. De plus, le schéma d'activation de la règle au sein du SBRF témoigne du comportement complexe, hautement non-linéaire, du bassin versant. Dans le cadre du SBRF, les mesures d'humidité du sol en des lieux représentatifs peuvent donc être utilisées comme une représentation de l'état actuel du système, ce qui permet une prévision de la transformation de la pluie en débit entièrement basée sur des données.Mots clefs logique floue; modélisation pluie-débit; humidité du sol; réflectométrie dans le domaine temporel (TDR)

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Local Linear Neuro-Fuzzy Models: Advanced Aspects

Nelles¹

2020

Nonlinear System Identification

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Automatische Strukturselektion für Fuzzy-Modelle zur Identifikation nichtlinearer, dynamischer Prozesse

Cited by 8 publications

References 0 publications

Nonlinear System Identification

Nonlinear System Identification

Fuzzy logic-based rainfall—runoff modelling using soil moisture measurements to represent system state

Local Linear Neuro-Fuzzy Models: Advanced Aspects

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