On the optimal weight of a perceptron with Gaussian data and arbitrary nonlinearity

dc.contributor.authorFeuer, Arie
dc.contributor.authorCristi, Roberto
dc.contributor.corporateNaval Postgraduate School (U.S.)en_US
dc.contributor.departmentElectrical and Computer Engineeringen_US
dc.date.accessioned2018-01-11T17:49:12Z
dc.date.available2018-01-11T17:49:12Z
dc.date.issued1993-06
dc.description.abstractIn this correspondence we investigate the solution to the following problem: Find the optimal weighted sum of given signals when the optimality criteria is the expected value of a function of this sum and a given "training" signal. The optimality criteria can be a nonlin­ear function from a very large family of possible functions. A number of interesting cases fall under this general framework, such as a single layer perceptron with any of the commonly used nonlinearities, the LMS, the LMF or higher moments, or the various sign algorithms. Assuming the signals to be jointly Gaussian we show that the optimal solution, when it exits, is always collinear with the well-known Wiener solution, and only its scaling factor depends on the particular functions chosen. We also present necessary constructive conditions for the existance of the optimal solution.en_US
dc.format.extent3 p.en_US
dc.identifier.citationA. Feuer, R. Cristi, "On the optimal weight of a perceptron with Gaussian data and arbitrary nonlinearity," IEEE Transactions on Signal Processing, v.41, no.6, (June 1993), pp. 2257-2259en_US
dc.identifier.urihttps://hdl.handle.net/10945/56651
dc.publisherIEEEen_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.en_US
dc.titleOn the optimal weight of a perceptron with Gaussian data and arbitrary nonlinearityen_US
dc.typeArticleen_US
dspace.entity.typePublication
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