Pulse density Hopfield neural network system with learning capability using FPGA
ACC'08 Proceedings of the WSEAS International Conference on Applied Computing Conference
Pulse density recurrent neural network systems with learning capability using FPGA
WSEAS Transactions on Circuits and Systems
On simultaneous perturbation particle swarm optimization
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part I
Learning scheme for complex neural networks using simultaneous perturbation
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
A scalable pipelined architecture for real-time computation of MLP-BP neural networks
Microprocessors & Microsystems
Global optimization using a multipoint type quasi-chaotic optimization method
Applied Soft Computing
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Recurrent neural networks have interesting properties and can handle dynamic information processing unlike ordinary feedforward neural networks. However, they are generally difficult to use because there is no convenient learning scheme. In this paper, a recursive learning scheme for recurrent neural networks using the simultaneous perturbation method is described. The detailed procedure of the scheme for recurrent neural networks is explained. Unlike ordinary correlation learning, this method is applicable to analog learning and the learning of oscillatory solutions of recurrent neural networks. Moreover, as a typical example of recurrent neural networks, we consider the hardware implementation of Hopfield neural networks using a field-programmable gate array (FPGA). The details of the implementation are described. Two examples of a Hopfield neural network system for analog and oscillatory targets are shown. These results show that the learning scheme proposed here is feasible.