Titlebook: Artificial Neural Networks for the Modelling and Fault Diagnosis of Technical Processes; Krzysztof Patan Book 2008 Springer-Verlag Berlin

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https://doi.org/10.1007/BFb0108459ccording to which any continuous non-linear relation can be approximated with arbitrary accuracy using a neural network with a suitable architecture and weight parameters. Their another attractive property is the self-learning ability. A neural network can extract the system features from historical

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S. Hunklinger,H. Sussner,K. Dransfeldion to training algorithms adjusting the parameters of neural networks. If the predictor is unstable for certain choices of neural model parameters, serious numerical problems can occur during training. Stability criteria should be universal, applicable to as broad a class of systems as possible and

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Rudolf Sizmann,Constantin Varelas of the residual signal takes place and, subsequently, the decision about faults is made in the form of an alarm. The residual evaluation is nothing else but a logical decision making process that transforms quantitative knowledge into qualitative . statements [4, 9, 7]. It can also be seen as a cla

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Diffraction in surfaces and interfaces,ee examples are considered:.In all case studies, locally recurrent globally feedforward networks, introduced in Section 3.5.4, are used as models of the industrial processes considered. Other types of neural networks, discussed in Sections 7.2 and 7.3.3, are used in decision making in order to detec

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Surface-dynamics of growing crystals,ing and identification of non-linear dynamic processes and fault diagnosis of technical processes. The self-learning ability and the property of approximating non-linear functions provide the modelling of non-linear systems with a great flexibility. These features allow one to design adaptive contro