Titlebook: Nonlinear System Identification; From Classical Appro Oliver Nelles Textbook 2020Latest edition The Editor(s) (if applicable) and The Autho

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Introduction,on, static models, dynamic models, and applications. The whole model development path in system identification from choice of the model inputs up to model validation is introduced. The recurring topic of “fiddle parameters” is addressed, which often is hidden but plays a crucial role in the real app

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Nonlinear Local Optimization of this chapter focuses on unconstrained optimization, but some basics also deal with constrained optimization. First, the exact criteria to optimize are investigated: Batch adaptation, sample adaptation, and mini-batch adaptation as a way in between are discussed. The role of the initial parameter

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Unsupervised Learning Techniqueselpful in solving a supervised learning problem efficiently. Typically they are considered not for their own sake but as a means for achieving something else. Therefore, it is sufficient to cover the most important approaches briefly. Two categories are discussed: (i) principal component analysis, w

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Model Complexity Optimizationhe machine learning approaches discussed in the remainder of this book. Finding a good model complexity is a crucial issue for all data-driven modeling, and many very distinct approaches exist for doing that. First, the fundamental tradeoff between bias and variance is illustrated and discussed from

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Linear, Polynomial, and Look-Up Table Modelsomial models, and (iii) look-up tables. Although all of them are not very well suited to deal with general complex nonlinear problems, it is very important to understand how they work, what their characteristics are, and when and why they typically fail. In particular, in the case of look-up tables,

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Neural Networks perceptron (MLP) network. Additionally, some network architectures of minor importance are also covered. A key topic of this chapter is the issue of how to map a high-dimensional input vector (or of hidden layer neurons) to a scalar quantity within each neuron of the network. The three common const

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