The Cybenko theorem is a theorem proved by George Cybenko in 1989 that says that a single hidden layer, feed forward neural network is capable of approximating any continuous, multivariate function to any desired degree of accuracy and that failure to map a function arises from poor choices for \mathbf{w}_1, \mathbf{w}_2, \dots , \mathbf{w}_N, \mathbf{\alpha}, and \mathbf{\theta} or an insufficient number of hidden neurons.

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  • The Cybenko theorem is a theorem proved by George Cybenko in 1989 that says that a single hidden layer, feed forward neural network is capable of approximating any continuous, multivariate function to any desired degree of accuracy and that failure to map a function arises from poor choices for \mathbf{w}_1, \mathbf{w}_2, \dots , \mathbf{w}_N, \mathbf{\alpha}, and \mathbf{\theta} or an insufficient number of hidden neurons. (en)
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  • The Cybenko theorem is a theorem proved by George Cybenko in 1989 that says that a single hidden layer, feed forward neural network is capable of approximating any continuous, multivariate function to any desired degree of accuracy and that failure to map a function arises from poor choices for \mathbf{w}_1, \mathbf{w}_2, \dots , \mathbf{w}_N, \mathbf{\alpha}, and \mathbf{\theta} or an insufficient number of hidden neurons. (en)
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  • Cybenko theorem (en)
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