| For Neural Networks, Function Determines Form (1992) | |||||||||||||||||||
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| This paper shows that the weights of continuous-time feedback neural networks are uniquely identifiable from input/output measurements. Under very weak genericity assumptions, the following is true: Assume given two nets, whose neurons all have the same nonlinear activation function oe; if the two nets have equal behaviors as "black boxes" then necessarily they must have the same number of neurons and ---except at most for sign reversals at each node--- the same weights. Key words: Neural networks, realization from input/output data, control systems Research supported in part by US Air Force Grant AFOSR-91-0343. Rutgers Center for Systems and Control May FOR NEURAL NETWORKS, FUNCTION DETERMINES FORM 1 Francesca Albertini Eduardo D. Sontag SYCON - Rutgers Center for Systems and Control Department of Mathematics, Rutgers University, New Brunswick, NJ 08903 (908) 932-3072, E-mail: albertin@hilbert.rutgers.edu, sontag@hilbert.rutgers.edu ABSTRACT This paper shows that the weights of ... | |||||||||||||||||||
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