Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory
Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory
Fault Diagnosis: Models, Artificial Intelligence, Applications
Fault Diagnosis: Models, Artificial Intelligence, Applications
Survey A review of methods for input/output selection
Automatica (Journal of IFAC)
Statistical active learning in multilayer perceptrons
IEEE Transactions on Neural Networks
Toward the training of feed-forward neural networks with the D-optimum input sequence
IEEE Transactions on Neural Networks
Stability Analysis and the Stabilization of a Class of Discrete-Time Dynamic Neural Networks
IEEE Transactions on Neural Networks
Selection of training data for locally recurrent neural network
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
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The problem of determining an optimal training schedule for a locally recurrent neural network is discussed. Specifically, the proper choice of the most informative measurement data guaranteeing the reliable prediction of the neural network response is considered. Based on a scalar measure of the performance defined on the Fisher information matrix related to the network parameters, the problem was formulated in terms of optimal experimental design. Then, its solution can be readily achieved via the adaptation of effective numerical algorithms based on the convex optimization theory. Finally, some illustrative experiments are provided to verify the presented approach.