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J. Ortega, A. Prieto, A. Lloris, F.J. Pelayo, "Generalized Hopfield Neural Network for Concurrent Testing," IEEE Transactions on Computers, vol. 42, no. 8, pp. 898912, August, 1993.  
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@article{ 10.1109/12.238481, author = {J. Ortega and A. Prieto and A. Lloris and F.J. Pelayo}, title = {Generalized Hopfield Neural Network for Concurrent Testing}, journal ={IEEE Transactions on Computers}, volume = {42}, number = {8}, issn = {00189340}, year = {1993}, pages = {898912}, doi = {http://doi.ieeecomputersociety.org/10.1109/12.238481}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Computers TI  Generalized Hopfield Neural Network for Concurrent Testing IS  8 SN  00189340 SP898 EP912 EPD  898912 A1  J. Ortega, A1  A. Prieto, A1  A. Lloris, A1  F.J. Pelayo, PY  1993 KW  generalised Hopfield neural network; performance evaluation; concurrent testing; checking circuitry; concurrent testable circuit; aliasing probability; spectral techniques; ReedMuller transform; energy function; associated energy function; polynomial dependence; combinatorial circuits; fault tolerant computing; Hopfield neural nets; logic testing. VL  42 JA  IEEE Transactions on Computers ER   
The use of generalized Hopfield neural networks in designing the checking circuitry of a concurrent testable circuit is discussed. The aliasing probability, a measure for evaluating the performance of the checking circuitry, is provided. It is shown how, by using spectral techniques based on the ReedMuller transform, the aliasing probability can be expressed as a function of the ReedMuller coefficients. Therefore, obtaining the checking circuitry means selecting a set of ReedMuller spectral coefficients, with fewer elements than a given bound, that minimizes the aliasing probability. To apply the neural networks to design the checking circuitry for concurrent testing, the aliasing probability has been used as an energy function, and the Hopfield neural network has been modified to have an associated energy function with any type of polynomial dependence on the processor states.
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