NDLI: A state-constrained model for cellular nonlinear network optimization

Content Provider	IEEE Xplore Digital Library
Author	Chou, E.Y. Sheu, B.J. Tsai, R.H.
Copyright Year	1992
Abstract	A G/sub m/C-style state constrained neuron (SCN) model for the design of processors in analog recurrent neural networks such as Hopfield neural networks, cellular nonlinear networks for combinatorial optimization is described. The unconstrained neurons which have the free state variable, could be stable at any arbitrary point in the solution space or trapped by un-intentional effects. These may introduce errors. For the unconstrained network, the solution could be different from the expected one due to the discrepancy in the energy function of the network and the objective function to be optimized. In addition, if the state variable is limited by some neighboring saturated transistors, un-desirable results may be obtained. The G/sub m/C-style SCN model can ensure the convergence of the network and avoid discrepancy between the energy function of the network and the objective function. The state resistor is also eliminated in the G/sub m/C model so that high cell-density can be achieved. Simulation results show that the proposed model is effective in significantly reducing optimization error.
Starting Page	445
Ending Page	449
Page Count	5
File Size	193369
File Format	PDF
ISSN	10577122
Volume Number	44
Issue Number	5
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1997-05-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Cellular networks Neurons Optimization methods Constraint optimization Resistors Design optimization Circuit stability Process design Recurrent neural networks Hopfield neural networks
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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