FPGA Implementation of a Recurrent Neural Fuzzy Network with On-Chip Learning for Prediction and Identification Applications

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Provided by: Nankai University
Topic: Hardware
Format: PDF
In this paper, a hardware implementation of a Recurrent Neural Fuzzy Network (RNFN) used for identification and prediction is proposed. A recurrent network is embedded in the RNFN by adding feedback connections in the second layer, where the feedback units act as memory elements. Although the Back Propagation (BP) learning algorithm is widely used in the RNFN, BP is too complicated to be implemented using hardware. However, the authors use the simultaneous perturbation method as a learning scheme for hardware implementation to overcome the above-mentioned problems.
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