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The 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 opti­ mization 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 ap­ proach for a broad variety of systems.

Die zuverlässige Versorgung einer hoch technisierten Gesellschaft mit elektrischer Energie ist von enormer Bedeutung. Der Bedarf an unterbrechungsfreiem Strom wächst beständig; gleichzeitig sind Forderungen nach Wirtschaftlichkeit, Effizienz, Kostengerechtigkeit und nicht zuletzt Umweltschutz zu erfüllen.Hinzu kommt, dass grundlegend veränderte rechtliche Rahmenbedingungen in Deutschland und Europa Netzbetreiber vor große Herausforderungen stellen: Die Netze müssen langfristig ausgebaut werden, um einen EU-weiten Strombinnenmarkt gewährleisten zu können. Kurz- bis mittelfristig ist durch Koordination mit anderen Netzbetreibern sicherzustellen, dass trotz neuer Transportfunktionen der Netze gravierende Stromausfälle ausbleiben.Die vorliegende Studie präsentiert das Resultat einer interdisziplinären Forschungsarbeit, die Ursachen von Instabilitäten, wesentlich beeinflusst durch den Trend zu einer weitergehenden Integration der europäischen Stromnetze, untersucht und die Verlässlichkeit der Energieversorgungsnetze reflektiert. Die Studie richtet sich mit konkreten Empfehlungen zu Investitions-, Sicherheits-, Effizienz- und Qualitätsaspekten der elektrischen Netze an Wissenschaft, Wirtschaft, Politik und die interessierte Öffentlichkeit. Ziel ist es, für anstehende politische Entscheidungen sowie administrative und unternehmerische Maßnahmen eine wertvolle Entscheidungsgrundlage zu bieten. So wird durch die fachübergreifende Analyse der netzwirtschaftlichen Pflichten und ihrer Regulierung eine Lücke in der wissenschaftlichen Betrachtung geschlossen und Impulse für die weitere Gestaltung der Elektrizitätsnetzwirtschaft gegeben.

The 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 opti­ mization 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 ap­ proach for a broad variety of systems.