Adding Decaying Self-feedback Continuous Hopfield Neural Network Convergence Analysis in the Hyper-cube Space

Title:

Adding Decaying Self-feedback Continuous Hopfield Neural Network Convergence Analysis in the Hyper-cube Space

Creator:

Fei, Chunguo and Su, Baili

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:dab0e878-5426-487d-b950-05eae3c0b30e
uuid:dab0e878-5426-487d-b950-05eae3c0b30e
doi:10.14311/NNW.2012.22.020

Subject:

Adding decaying self-feedback continuous Hopefield neural network, convergence analysis, piecewise linear function, and hyper-cube

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

After sigmoid activation function is replaced with piecewise linear activation function, the adding decaying self-feedback continuous Hopfield neural network (ADSCHNN) searching space changes to hyper-cube space, i.e. the simplified ADSCHNN is obtained. Then, convergence analysis is given for the simplified ADSCHNN in hyper-cube space. It is proved through convergence analysis that the ADSCHNN outperforms the continuous Hopfield neural network (CHNN), when they are applied to solve optimization problem. It is also proved that when extra self-feedback is negative, the ADSCHNN is more effective than the extra self-feedback is positive, when the ADSCHNN is applied to solve TSP.

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 | 2012 Volume:22 | Number:4

Harvested from:

CDK

Metadata only:

false