A learning rule of neural networks via simultaneous perturbation and its hardware implementation
TLDR
A learning rule of neural networks via a simultaneous perturbation and an analog feedforward neural network circuit using the learning rule, which requires only forward operations of the neural network and is suitable for hardware implementation.About:
This article is published in Neural Networks.The article was published on 1995-02-01 and is currently open access. It has received 106 citations till now. The article focuses on the topics: Learning rule & Competitive learning.read more
Citations
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Journal ArticleDOI
A one-measurement form of simultaneous perturbation stochastic approximation
TL;DR: This note presents a form of SPSA that requires only one function measurement (for any dimension), and theory is presented that identifies the class of problems for which this one-measurement form will be asymptotically superior to the standard two-measuresment form.
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A Circuit-Based Learning Architecture for Multilayer Neural Networks With Memristor Bridge Synapses
TL;DR: It is shown that a circuit-based learning using RWC is two orders faster than its software counterpart, which is a first of its kind demonstrating successful circuit- based learning for multilayer neural network built with memristors.
Journal ArticleDOI
Learning rules for neuro-controller via simultaneous perturbation
TL;DR: Learning rules using simultaneous perturbation for a neurocontroller that controls an unknown plant using a kind of the gradient method as a learning rule of the neural network is described.
Journal ArticleDOI
Optimal random perturbations for stochastic approximation using a simultaneous perturbation gradient approximation
P. Sadegh,James C. Spall +1 more
TL;DR: In this article, the authors derived the optimal distribution for the components of the simultaneous perturbation vector in the Monte Carlo process and showed that it is a symmetric Bernoulli.
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Simultaneous perturbation learning rule for recurrent neural networks and its FPGA implementation
Yutaka Maeda,M. Wakamura +1 more
TL;DR: This paper considers the hardware implementation of Hopfield neural networks using a field-programmable gate array (FPGA) and shows that the learning scheme proposed here is feasible.
References
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Journal ArticleDOI
Multivariate stochastic approximation using a simultaneous perturbation gradient approximation
TL;DR: The paper presents an SA algorithm that is based on a simultaneous perturbation gradient approximation instead of the standard finite-difference approximation of Keifer-Wolfowitz type procedures that can be significantly more efficient than the standard algorithms in large-dimensional problems.
Journal ArticleDOI
Accelerating the convergence of the back-propagation method
TL;DR: The back-propagation algorithm described by Rumelhart et al. (1986) can greatly accelerate convergence as discussed by the authors, however, in many applications, the number of iterations required before convergence can be large.
Book
Analog VLSI implementation of neural systems
Carver A. Mead,Mohammed Ismail +1 more
TL;DR: A Neural Processor for Maze Solving and Issues in Analog VLSI and MOS Techniques for Neural Computing are discussed.
Journal ArticleDOI
Weight perturbation: an optimal architecture and learning technique for analog VLSI feedforward and recurrent multilayer networks
Marwan A. Jabri,Barry Flower +1 more
TL;DR: It is shown that using gradient descent with direct approximation of the gradient instead of back-propagation is more economical for parallel analog implementations and is suitable for multilayer recurrent networks as well.
Proceedings Article
A Fast Stochastic Error-Descent Algorithm for Supervised Learning and Optimization
TL;DR: A parallel stochastic algorithm is investigated for error-descent learning and optimization in deterministic networks of arbitrary topology based on the model-free distributed learning mechanism of Dembo and Kailath and supported by a modified parameter update rule.