Convergence Analysis of Adaptive Recurrent Neural Network
نویسندگان
چکیده
This paper presents analysis of a modified Feed Forward Multilayer Perceptron (FMP) by inserting an ARMA (Auto Regressive Moving Average) model at each neuron (processor node) with the Backp ropagation learning algorithm. The stability analysis is presented to establish the convergence theory of the Back propagation algorithm based on the Lyapunov function. Furthermore, the analysis extends the Back propagation learning rule by introducing the adaptive learning factors. A range of possible learning factors is derived from the stability analysis. Performance of such network learning with adaptive learning factors is presented and demonstrates that the adaptive learning factor enhance the performance of training while avoiding oscillation phenomenon. Keywords–Adaptive learning, Back propagation, Neural networks, Stability analysis
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