An LMS Algorithm for Training Single Layer Globally Recursive Neural Networks
نویسندگان
چکیده
Unlike feedforward neural networks (FFNN) which can act as universal function approximaters, recursive neural networks have the potential to act as both universal function approximaters and universal system approximaters. In this paper, a globally recursive neural network least mean square (GRNNLMS) gradient descent or a real time recursive backpropagation (RTRBP) algorithm is developed for a single layer globally recursive neural network that has multiple delays in its feedback path. KeywordsLeast mean square, real-time recursive backpropagation, recurrent neural network, neural network training, globally recursive neural network
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تاریخ انتشار 1998