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
Implementation of the simultaneous perturbation algorithm for stochastic optimization
TL;DR: This paper presents a simple step-by-step guide to implementation of SPSA in generic optimization problems and offers some practical suggestions for choosing certain algorithm coefficients.
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Artificial neural networks in hardware: A survey of two decades of progress
Janardan Misra,Indranil Saha +1 more
TL;DR: This article presents a comprehensive overview of the hardware realizations of artificial neural network models, known as hardware neural networks (HNN), appearing in academic studies as prototypes as well as in commercial use.
Posted Content
A Survey of Neuromorphic Computing and Neural Networks in Hardware.
Catherine D. Schuman,Thomas E. Potok,Robert M. Patton,J. Douglas Birdwell,Mark Edward Dean,Garrett S. Rose,James S. Plank +6 more
TL;DR: An exhaustive review of the research conducted in neuromorphic computing since the inception of the term is provided to motivate further work by illuminating gaps in the field where new research is needed.
An Overview of the Simultaneous Perturbation Method for Efficient Optimization
TL;DR: Simultaneous perturbation stochastic approximation (SPSA) as mentioned in this paper is a widely used method for multivariate optimization problems that requires only two measurements of the objective function regardless of the dimension of the optimization problem.
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Memristor Bridge Synapse-Based Neural Network and Its Learning
TL;DR: The use of memristor bridge synapse in the proposed architecture solves one of the major problems, regarding nonvolatile weight storage in analog neural network implementations, and a modified chip-in-the-loop learning scheme suitable for the proposed neural network architecture is proposed.
References
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Novel learning method for analogue neural networks
T. Matsumoto,M. Koga +1 more
TL;DR: An analogue-neural-network learning method based on hardware is proposed, where all the network parameters are oscillated with slightly different frequencies, and the spectra appearing in the error signal are used to change the parameters.
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Trial-and-error correlation learning
TL;DR: Computer simulation shows that a new learning architecture for hardware implementation of neural networks surpasses BP learning in converging to the global minimum when the trial-and-error correlation is defined so as to emphasize the gain rather than the loss.
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Learning rules of neural networks for inverse systems
TL;DR: Rules with the perturbation on an input of the unknown system and a rule with it on each weight of the neural network are presented and the usefulness of these learning rules is confirmed via some numerical simulations.