Journal of VLSI Signal Processing Systems
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
Hardware Neural Network for a Visual Inspection System
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
On simultaneous perturbation particle swarm optimization
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Learning scheme for complex neural networks using simultaneous perturbation
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
FPGA realization of a radial basis function based nonlinear channel equalizer
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
FPGA implementation of a wavelet neural network with particle swarm optimization learning
Mathematical and Computer Modelling: An International Journal
International Journal of High Performance Systems Architecture
ACM Transactions on Reconfigurable Technology and Systems (TRETS)
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Hardware realization is very important when considering wider applications of neural networks (NNs). In particular, hardware NNs with a learning ability are intriguing. In these networks, the learning scheme is of much interest, with the backpropagation method being widely used. A gradient type of learning rule is not easy to realize in an electronic system, since calculation of the gradients for all weights in the network is very difficult. More suitable is the simultaneous perturbation method, since the learning rule requires only forward operations of the network to modify weights unlike the backpropagation method. In addition, pulse density NN systems have some promising properties, as they are robust to noisy situations and can handle analog quantities based on the digital circuits. We describe a field-programmable gate array realization of a pulse density NN using the simultaneous perturbation method as the learning scheme. We confirm the viability of the design and the operation of the actual NN system through some examples.