About: Nonlinear system identification

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System identification is a method of identifying or measuring the mathematical model of a system from measurements of the system inputs and outputs. The applications of system identification include any system where the inputs and outputs can be measured and include industrial processes, control systems, economic data, biology and the life sciences, medicine, social systems and many more. 1. * Volterra series models, 2. * Block-structured models, 3. * Neural network models, 4. * NARMAX models, and 5. * State-space models.

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rdfs:label	Nonlinear system identification (en)
rdfs:comment	System identification is a method of identifying or measuring the mathematical model of a system from measurements of the system inputs and outputs. The applications of system identification include any system where the inputs and outputs can be measured and include industrial processes, control systems, economic data, biology and the life sciences, medicine, social systems and many more. 1. * Volterra series models, 2. * Block-structured models, 3. * Neural network models, 4. * NARMAX models, and 5. * State-space models. (en)
dcterms:subject	Dynamical systems Nonlinear systems
Wikipage page ID	40158142 (xsd:integer)
Wikipage revision ID	1118525242 (xsd:integer)
Link from a Wikipage to another Wikipage	Neural network Particle filter Biology Convolution Maximum likelihood estimation Noise Class (set theory) Control system Approximation Likelihood function Artificial neural networks Feature selection Pattern recognition Superposition principle Weierstrass Principal component analysis Statistical Model Dynamical systems Nonlinear systems Chaos theory Bifurcation theory System Economic data Grey box model Independence (probability theory) System identification Mathematical model Medicine State-space representation Social system Volterra series Expectation–maximization algorithm Wiener series Stochastic process Nonlinear system Supervised learning Life sciences Wavelets Industrial process Subharmonics
Link from a Wikipage to an external page	https://books.google.com/books%3Fid=1O7lBwAAQBAJ&printsec=frontcover%23v=onepage&q&f=false https://books.google.com/books%3Fid=Os7nCwAAQBAJ&printsec=frontcover%23v=onepage&q&f=false
sameAs	Nonlinear system identification Nonlinear system identification Nonlinear system identification Nonlinear system identification Nonlinear system identification
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has abstract	System identification is a method of identifying or measuring the mathematical model of a system from measurements of the system inputs and outputs. The applications of system identification include any system where the inputs and outputs can be measured and include industrial processes, control systems, economic data, biology and the life sciences, medicine, social systems and many more. A nonlinear system is defined as any system that is not linear, that is any system that does not satisfy the superposition principle. This negative definition tends to obscure that there are very many different types of nonlinear systems. Historically, system identification for nonlinear systems has developed by focusing on specific classes of system and can be broadly categorized into five basic approaches, each defined by a model class: 1. * Volterra series models, 2. * Block-structured models, 3. * Neural network models, 4. * NARMAX models, and 5. * State-space models. There are four steps to be followed for system identification: data gathering, model postulate, parameter identification and model validation. Data gathering is considered as the first and essential part in identification terminology, used as the input for the model which is prepared later. It consists of selecting an appropriate data set, pre-processing and processing. It involves the implementation of the known algorithms together with the transcription of flight tapes, data storage and data management, calibration, processing, analysis and presentation. Moreover, model validation is necessary to gain confidence in, or reject, a particular model. In particular, the parameter estimation and the model validation are integral parts of the system identification. Validation refers to the process of confirming the conceptual model and demonstrating an adequate correspondence between the computational results of the model and the actual data. (en)
gold:hypernym	Method
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is Link from a Wikipage to another Wikipage of	Neural network Estimation theory Data analysis Big data Black box model of power converter Digital signal processing Grey box model Ordinary least squares System identification Mathematical model Nonlinear system
is foaf:primaryTopic of	wikipedia-en:Nonlinear_system_identification

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