Bibliografia
- [1] A. R. Barron, “Universal approximation bounds for superpositions of a sigmoidal function”, IEEE Trans. on Information Theory, vol. 39, pp. 930-945, 1993.
- [2] Girosi, F., & Anzellotti, (1993). Rates of convergence for radial basis function and neural networks. In Artificial Neural Networks for Speech and Vision (pp. 97-113). London: Chapman & Hall.
- [3] Kurková, V. (1992). Kolmogorov’s theorem and multilayer neural networks. Neural Networks, 5, pp. 501-506.
- [4] Mhaskar, H. N., & Micchelli, C. A. (1994). Dimension-independent bound on the degree of approximation by Neural Networks. IBM Journal of Research and Development, 38, pp. 277-284.
- [5] T. Parisini and R. Zoppoli, “Neural Network for feed-back feedforward non linear control systems”, IEEE Trans. on Neural Networks, vol. 5, pp. 436-449, 1994.
- [6] T. Parisini and R. Zoppoli, “Neural approximation for multistage optimal control of nonlinear stochastic systems”, IEEE Trans. on Automatic Control, vol. 41, pp. 889-895, 1996.
- [7] T. Parisini and R. Zoppoli, “Neural approximation for infinite-horizon optimal control of nonlinear stochastic systems”, IEEE Trans. on Neural Networks, vol. 9, pp. 1388-1408, 1998.
- [8] MATLAB® User’s Guide (Version 5.1) by MathWorks, Inc., 1998.
- [9] Introduction to Matlab for Engineers, The McGraw-Hill Companies, Inc. 1998.
- [10] H. Schildt, The C Complete Reference, third edition. The McGraw-Hill Companies, Inc. 1995.
Ringraziamenti,
Introduzione,
Capitolo 1,
Capitolo 2,
Capitolo 3,
Capitolo 4,
Bibliografia