| For neural networks, function determines form (1993) | |||||||||||||||||
Abstract | |||||||||||||||||
| 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 σ; 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. Moreover, even if the activations are not a priori known to coincide, they are shown to be also essentially determined from the external measurements. Key words: Neural networks, identification from input/output data, control systems 1 | |||||||||||||||||
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