Training Set Ranking and Selection Using Fuzzy Logic for Dynamic Plant Identifiers

As the training set is one of the critical sections in neural network domain, generating of it with prior knowledge can be extremely efficient. In this paper we have tried to explore the potential of using previously selected training set for the training of dynamic neural network. The neural network was used as the core of identifier which tries to identify the internal behavior of structure-unknown non-linear time variant dynamic system. In this regard we extract some features from each training set, in frequency and stochastic domain and consequently set a rank for each. The selected training set is the one which got highest rank. We use industrial robot manipulator for the case study. It is shown that, using this approach, the error rate of modeling has been decreased and therefore the identifier performance and resolution increase to the levels which gained by using fully random generated signals as training set.