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

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Artificial Neural Networks for the Modelling and Fault Diagnosis of Technical Processes

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Approximation Abilities of Locally Recurrent Networks,tem [39], or model based fault diagnosis of sensor and actuator faults in a sugar evaporator [26]. Tsoi and Back [38] compared and applied different architectures of locally recurrent networks to the prediction of speech utterance. Finally, Campolucci and Piazza [156] elaborated an intristic stabili

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Optimum Experimental Design for Locally Recurrent Networks,al conditions in order to gather informative measurements can be very expensive or even impossible (e.g. for faulty system states). On the other hand, data from a real-world system may be very noisy and using all the data available may lead to significant systematic modelling errors. As a result, we

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