Finite convergence of a fuzzy δ rule for a fuzzy perceptron

Title:

Finite convergence of a fuzzy δ rule for a fuzzy perceptron

Creator:

Li, Long, Yang, Jie, Liu , Yan, and Wu , Wei

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:796b9477-c3ce-4fd4-9eb1-112f779d7df4
uuid:796b9477-c3ce-4fd4-9eb1-112f779d7df4

Subject:

Fuzzy perceptron, learning algorithm, fuzzily separable, and finite convergence

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

This paper considers a fuzzy perceptron that has the same topological structure as the conventional linear perceptron. A learning algorithm based on a fuzzy δ rule is proposed for this fuzzy perceptron. The inner operations involved in the working process of this fuzzy perceptron are based on the max-min logical operations rather than conventional multiplication and summation, etc. The initial values of the network weights are fixed as 1. It is shown that each network weight is non-increasing in the training process and remains unchanged once it is less than 0.5. The learning algorithm has an advantage, as proved in this paper, that it converges in a finite number of steps if the training patterns are fuzzily separable. This result generalizes a corresponding classical result for conventional linear perceptrons. Some numerical experiments for the learning algorithm are provided to support our theoretical findings.

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 | 2008 Volume:18 | Number:6

Harvested from:

CDK

Metadata only:

false