Multilayer feedforward networks are universal approximators
Tài liệu tham khảo
Billingsley, 1979
Cybenko, 1988, Approximation by superpositions of a sigmoidal function
Dugundji, 1966
Gallant, 1988, There exists a neural network that does not make avoidable mistables, I:657
Grenander, 1981
Halmos, 1974
Hecht-Nielsen, 1987, Kolmogorov's mapping neural network existence theorem, III:11
Hecht-Nielsen, 1989, Theory of the back propagation neural network, I:593
Hornik, 1988, Multilayer feedforward networks are universal approximators
1987
1988
Irie, 1988, Capabilities of three layer perceptrons, I:641
Kolmogorov, 1957, On the representation of continuous functions of many variables by superposition of continuous functions of one variable and addition, Doklady Akademii Nauk SSR, 114, 953
Kolmogorov, 1961, ϵ-entropy and ϵ-capacity of sets in functional spaces, American Mathematical Society Translations, 2, 277, 10.1090/trans2/017/10
Lapedes, 1988, How neural networks work
le Cun, 1987, Modeles connexionistes de l'apprentissage
Lorentz, 1976, The thirteenth problem of Hilbert, Vol. 28, 419
Maxwell, 1986, Nonlinear dynamics of artificial neural systems
Minsky, 1969
Rudin, 1964
Severini, 1987, Convergence rates of maximum likelihood and related estimates in general parameter spaces
Stinchcombe, 1989, Universal approximation using feedforward networks with non-sigmoid hidden layer activation functions, I:613
White, 1988, The case for conceptual and operational separation of network architectures and learning mechanisms
White, 1988, Multilayer feedforward networks can learn arbitrary mappings: Connectionist nonparametric regression with automatic and semi-automatic determination of network complexity
White, H., & Wooldridge, J. M. (in press). Some results for sieve estimation with dependent observations. In W. Barnett, J. Powell, & G. Tauchen (Eds.), Nonparametric and semi-parametric methods in econometrics and statistic. New York: Cambridge University Press.
Williams, 1986, The logic of activation functions, Vol. 1, 423