Universal Approximation Theorem for Interval Neural Networks

Reliable Computing

Volumn 10, Issue 2, 1998, Pages 235-239

  Baker, Mark R ^a   Patil, Rajendra B ^b,c  

^a Geomedia Research and Development   (United States)

^b LOS ALAMOS NATIONAL LABORATORY   (United States)

^c GENERAL ELECTRIC COMPANY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 33845880200     PISSN: 13853139     EISSN: None     Source Type: Journal    
DOI: 10.1023/a:1009951412412     Document Type: Article

References (10)

1. Alefeld, G. and Herzberger, J.: Introduction to Interval Computations, Academic Press, NY, 1983.
2. Haykin, S.: Neural Networks: A Comprehensive Foundation, Macmillan College Publishing Company, New York, NY, 1994.
3. Hecht-Nielsen, R.: Kolmogorov's Mapping Neural Network Existence Theorem, in: Proceedings of First IEEE International Conference on Neural Networks, San Diego, CA, 1987, pp. 11-14.
4. Homik, K., Stinchcombe, M., and White, H.: Multilayer Feedforward Neural Networks Are Universal Approximators, Neural Networks 2 (1989), pp. 359-366.
5. Hornik, K.: Approximation Capabilities of Multilayer Feedforward Neural Networks. Neural Networks 4 (1991), pp. 251-257.
6. Ishibuchi, H. and Tanaka, H.: An Architecture of Neural Networks with Interval Weights and Its Application to Fuzzy Regression, Fuzzy Sets and Systems 57 (1993), pp. 27-39.
7. Kůrková, V.: Kolmogorov's Theorem Is Relevant, Neural Computation 3 (1991), pp. 617-622.
8. Kůrková, V.: Kolmogorov's Theorem and Multilayer Neural Networks, Neural Networks 5 (1992), pp. 501-506.
9. Nesterov, V. M.: Interval Analogues of Hubert's 13th Problem, in: Abstracts of the Int'l Conference Interval '94, St. Petersburg, Russia, March 7-10, 1994, pp. 185-186.
10. Patil, R. B.: Interval Neural Networks, in: Extended Abstracts of APIC'95: International Workshop on Applications of Interval Computations, EI Paso. TX, Febr. 23-25, 1995, Reliable Computing (1995), Supplement, p. 164.