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For training algorithms of recurrent neural networks (RNN), convergent speed and training error are always two contradictory performances. In this letter, we propose a normalized adaptive recurrent learning (NARL) to obtain a tradeoff between transient and steady-state response. An augmented term is added to error gradient to exactly model the derivative of(More)
In the past decades, Recurrent Neural Network (RNN) has attracted extensive research interests in various disciplines. One important motivation of these investigations is the RNN's promising ability of modeling time-behavior of nonlinear dynamic systems. It has been theoretically proved that RNN is able to map arbitrary input sequences to output sequences(More)
Chaotic neural networks have been successfully applied in pattern association problems in many research. However there are few in-depth theoretical analysis for such networks, such as stability issues. In this paper, we propose a new type of chaotic recurrent neural network (CRNN) which is more powerful in pattern association comparing to previous work.(More)
Recently repetitive controller has been applied in servo systems of hard disk drives (HDD) to remove the repeatable errors caused by rotating mechanism of spindle motors. However, most results of published articles are too complex to be implemented in industrial applications. In this paper, we present an improved design strategy for repetitive control of(More)
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