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JACIII Vol.24 No.5 pp. 593-598
doi: 10.20965/jaciii.2020.p0593
(2020)

Paper:

How to Describe Conditions Like 2-out-of-5 in Fuzzy Logic: A Neural Approach

Olga Kosheleva*, Vladik Kreinovich**, and Hoang Phuong Nguyen***,†

*Department of Teacher Education, University of Texas at El Paso
500 West University Avenue, El Paso, Texas 79968, USA

**Department of Computer Science, University of Texas at El Paso
500 West University Avenue, El Paso, Texas 79968, USA

***Division Informatics, Math-Informatics Faculty, Thang Long University
Nghiem Xuan Yem Road, Hoang Mai District, Hanoi, Vietnam

Corresponding author

Received:
March 9, 2020
Accepted:
April 15, 2020
Published:
September 20, 2020
Keywords:
fuzzy logic, neural networks, 2-out-of-5 conditions, medical applications
Abstract

In many medical applications, we diagnose a disease and/or apply a certain remedy if, e.g., two out of five conditions are satisfied. In the fuzzy case, i.e., when we only have certain degrees of confidence that each of n statement is satisfied, how do we estimate the degree of confidence that k out of n conditions are satisfied? In principle, we can get this estimate if we use the usual methodology of applying fuzzy techniques: we represent the desired statement in terms of “and” and “or,” and use fuzzy analogues of these logical operations. The problem with this approach is that for large n, it requires too many computations. In this paper, we derive the fastest-to-compute alternative formula. In this derivation, we use the ideas from neural networks.

Cite this article as:
O. Kosheleva, V. Kreinovich, and H. Nguyen, “How to Describe Conditions Like 2-out-of-5 in Fuzzy Logic: A Neural Approach,” J. Adv. Comput. Intell. Intell. Inform., Vol.24, No.5, pp. 593-598, 2020.
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Last updated on Dec. 01, 2020