Nonlinear switched capacitor 'neural' networks for optimization problems

A systematic approach is presented for the design of analog neural nonlinear programming solvers using switched-capacitor (SC) integrated circuit techniques. The method is based on formulating a dynamic gradient system whose state evolves in time toward the solution point of the corresponding progra...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems 1990-03, Vol.37 (3), p.384-398
Main Authors: Rodriguez-Vazquez, A., Dominguez-Castro, R., Rueda, A., Huertas, J.L., Sanchez-Sinencio, E.
Format: Article
Language:English
Subjects:
Analog computers
Applied sciences
Biological system modeling
Biology computing
Circuit simulation
Clocks
Cost function
Dynamic programming
Electric, optical and optoelectronic circuits
Electronics
Exact sciences and technology
Linear programming
Multidimensional systems
Neural networks
Switched capacitor networks
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Summary:A systematic approach is presented for the design of analog neural nonlinear programming solvers using switched-capacitor (SC) integrated circuit techniques. The method is based on formulating a dynamic gradient system whose state evolves in time toward the solution point of the corresponding programming problem. A neuron cell for the linear and the quadratic problem suitable for monolithic implementation is introduced. The design of this neuron and its corresponding synapses using SC techniques is considered in detail. An SC circuit architecture based on a reduced set of basic building blocks with high modularity is presented. Simulation results using a mixed-mode simulator (DIANA) and experimental results from breadboard prototypes are included, illustrating the validity of the proposed techniques.< >
ISSN:0098-4094
1558-1276
DOI:10.1109/31.52732