Novel Hybrid Rule Network Based on TS Fuzzy Rules

Title:

Novel Hybrid Rule Network Based on TS Fuzzy Rules

Creator:

Guo, Feng, Lin , Lin, Xie, Xiaolong, and Luo , Bin

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:98d3c726-6160-4142-91c8-e7e06cbfcfe3
uuid:98d3c726-6160-4142-91c8-e7e06cbfcfe3
doi:10.14311/NNW.2015.25.005

Subject:

Hybrid rule network, dynmic rule selection mechanism, fuzzy classification, and prediction

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

A novel hybrid rule network based on TS fuzzy rules is proposed to resolve the problems of fuzzy classification and prediction. The proposed model learns by using genetic algorithm and is able to cover the whole distribution regions of the samples. In the learning process: (1) fuzzy intervals of each dimension of the samples are partitioned evenly; (2) computing intervals (CIs) are established based on the even intervals; (3) linear weighted model of several normal probability distributions is used to describe the sample probability distribution on CIs; (4) membership degree of each CI is learnt to evaluate the importance of each CI, avoiding the problem that the optimal intervals are difficult to cover the original sample spaces; (5) dynamic rule selection mechanism is used to dynamically combine a small number of optimal rules linearly to achieve nonlinear approximation, reducing the computation load. Three experiments are performed: the experiments on Iris and Mackey-Glass chaotic time series show that HRN can achieve satisfactory results and is more effective in terms of generalization ability, whereas the experiment on exhaust gas temperature demonstrates that HRN can predict the EGT of aero engine effectively.

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 | 2015 Volume:25 | Number:1

Harvested from:

CDK

Metadata only:

false