Some new results on neural network approximation
1993; Elsevier BV; Volume: 6; Issue: 8 Linguagem: Inglês
10.1016/s0893-6080(09)80018-x
ISSN1879-2782
Autores Tópico(s)Stochastic Gradient Optimization Techniques
ResumoWe show that standard feedforward networks with as few as a single hidden layer can uniformly approximate continuous functions on compacta provided that the activation function ψ is locally Riemann integrable and nonpolynomial, and have universal Lp (μ) approximation capabilities for finite and compactly supported input environment measures μ provided that ψ is locally bounded and nonpolynomial. In both cases, the input-to-hidden weights and hidden layer biases can be constrained to arbitrarily small sets; if in addition ψ is locally analytic a single universal bias will do.
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