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Author Albertini, Francesca ♦ Sontag, Eduardo D.
Source CiteSeerX
Content type Text
Publisher IEEE Publications
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Black Box ♦ Control System ♦ Input Output Measurement ♦ Continuous-time Feedback Neural Network ♦ Neural Network ♦ Nonlinear Activation Function ♦ Weak Genericity Assumption ♦ Equal Behavior ♦ Input Output Data ♦ Function Determines Form ♦ External Measurement ♦ Key Word ♦ Sign Reversal
Description Proc. IEEE Conf. Decision and Control
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
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 1993-01-01