Neural nets and normal forms from fuzzy logic point of view

Title:

Neural nets and normal forms from fuzzy logic point of view

Creator:

Perfilieva, I.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:4a46cc68-eecd-40e7-8d15-eaaae7eab1f4
uuid:4a46cc68-eecd-40e7-8d15-eaaae7eab1f4

Subject:

disjunctive, conjunctive and additive normal forms, BL-logic, Łukasiewicz logic, ŁII logic, fuzzy logic, fuzzy relation, and approximation

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

The paper addresses the problém of efficient and adequate representation of functions using two soft computing techniques: fuzzy logic and neural networks. The principle approach to the construction of approximating formulas is discussed. We suggest a generalized definition of the normál forms in predicate BL and ŁII logic and prove conditional equivalence between a formula and each of its normal forms. Some mutual relations between the normál forms will be also established.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2001 Volume:11 | Number:6

Harvested from:

CDK

Metadata only:

false