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Record 50 of 1712
Electronic neural network for dynamic resource allocation
Author and Affiliation:
Thakoor, A. P.(Jet Propulsion Lab., California Inst. of Tech., Pasadena, CA, United States)
Eberhardt, S. P.(Jet Propulsion Lab., California Inst. of Tech., Pasadena, CA, United States)
Daud, T.(JPL, Pasadena, CA, United States)
Abstract: A VLSI implementable neural network architecture for dynamic assignment is presented. The resource allocation problems involve assigning members of one set (e.g. resources) to those of another (e.g. consumers) such that the global 'cost' of the associations is minimized. The network consists of a matrix of sigmoidal processing elements (neurons), where the rows of the matrix represent resources and columns represent consumers. Unlike previous neural implementations, however, association costs are applied directly to the neurons, reducing connectivity of the network to VLSI-compatible 0 (number of neurons). Each row (and column) has an additional neuron associated with it to independently oversee activations of all the neurons in each row (and each column), providing a programmable 'k-winner-take-all' function. This function simultaneously enforces blocking (excitatory/inhibitory) constraints during convergence to control the number of active elements in each row and column within desired boundary conditions. Simulations show that the network, when implemented in fully parallel VLSI hardware, offers optimal (or near-optimal) solutions within only a fraction of a millisecond, for problems up to 128 resources and 128 consumers, orders of magnitude faster than conventional computing or heuristic search methods.
Publication Date: Jan 01, 1991
Document ID:
19920034994
(Acquired Nov 22, 1995)
Accession Number: 92A17618
Subject Category: CYBERNETICS
Report/Patent Number: AIAA PAPER 91-3755
Document Type: Conference Paper
Publication Information: (SEE A92-17576)
Publisher Information: United States
Meeting Information: AIAA Computing in Aerospace Conference; 8th; Oct. 21-24, 1991; Baltimore, MD; United States
Financial Sponsor: NASA; United States
Organization Source: Jet Propulsion Lab., California Inst. of Tech.; Pasadena, CA, United States
Description: 9p; In English
Distribution Limits: Unclassified; Publicly available; Unlimited
Rights: Copyright
NASA Terms: ARCHITECTURE (COMPUTERS); NEURAL NETS; VERY LARGE SCALE INTEGRATION; BLOCK DIAGRAMS; HARDWARE
Imprint And Other Notes: IN: AIAA Computing in Aerospace Conference, 8th, Baltimore, MD, Oct. 21-24, 1991, Technical Papers. Vol. 1 (A92-17576 05-61). Washington, DC, American Institute of Aeronautics and Astronautics, 1991, p. 339-347. Research supported by SDIO and DARPA.
Availability Source: Other Sources
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