A one-measurement form of simultaneous perturbation stochastic approximation
Automatica (Journal of IFAC)
VLSI-Compatible Immplementations for Artificial Neural Networks
VLSI-Compatible Immplementations for Artificial Neural Networks
Neural Information Processing and VLSI
Neural Information Processing and VLSI
A Fast Stochastic Error-Descent Algorithm for Supervised Learning and Optimization
Advances in Neural Information Processing Systems 5, [NIPS Conference]
A Parallel Gradient Descent Method for Learning in Analog VLSI Neural Networks
Advances in Neural Information Processing Systems 5, [NIPS Conference]
An analog VLSI recurrent neural network learning a continuous-time trajectory
IEEE Transactions on Neural Networks
Toward a general-purpose analog VLSI neural network with on-chip learning
IEEE Transactions on Neural Networks
Implementations of artificial neural networks using current-mode pulse width modulation technique
IEEE Transactions on Neural Networks
Learning rules for neuro-controller via simultaneous perturbation
IEEE Transactions on Neural Networks
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
Learning scheme for complex neural networks using simultaneous perturbation
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
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The choice of the learning scheme is very important in the implementation of neural networks to take advantage of their learning ability. Usually, the back-propagation method is widely used as a learning rule in neural networks. Since back-propagation requires so-called error back propagation to update weights, it is relatively difficult to realize hardware neural networks using the back-propagation method. In this paper, we present a pulse density neural network system with learning ability. As a learning rule, the simultaneous perturbation method is used. The learning rule requires only forward operations of networks to update weights instead of the error back-propaga- tion. Thus, we can construct the network system with learning ability without the need for a complicated circuit that calculates gradients of an error function. Pulse density is used to represent the basic quantities in this system. The pulse system has some attractive properties which includes robustness against a noisy environment. A combina- tion of the simultaneous perturbation learning rule and the pulse density system results in an interesting architec- ture of hardware neural systems. Results for the exclusive OR problem and a simple identity problem are shown.