A hybrid approach based on tissue P systems and artificial bee colony for IIR system identification

A hybrid approach based on tissue P systems and artificial bee colony for IIR system identification This paper presents a hybrid approach for infinite impulse response (IIR) system identification, called ABC-PS, that combines artificial bee colony (ABC) and tissue P systems. A tissue P system with fully connected structure of cells has been considered as its computing framework. A modification of ABC was developed as evolution rules for objects according to fully connected structure and communication mechanism. With the control of the object’s evolution-communication mechanism, the tissue P system designed can effectively and efficiently identify the optimal filter coefficients for an IIR system. The performance of ABC-PS was compared with artificial bee colony and several other evolutionary algorithms. Simulation results show that ABC-PS is superior or comparable to the other algorithms for the employed examples and can be efficiently used for IIR system identification. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Neural Computing and Applications Springer Journals

A hybrid approach based on tissue P systems and artificial bee colony for IIR system identification

Loading next page...
 
/lp/springer_journal/a-hybrid-approach-based-on-tissue-p-systems-and-artificial-bee-colony-X4aRlBEAD8
Publisher
Springer London
Copyright
Copyright © 2016 by The Natural Computing Applications Forum
Subject
Computer Science; Artificial Intelligence (incl. Robotics); Data Mining and Knowledge Discovery; Probability and Statistics in Computer Science; Computational Science and Engineering; Image Processing and Computer Vision; Computational Biology/Bioinformatics
ISSN
0941-0643
eISSN
1433-3058
D.O.I.
10.1007/s00521-016-2201-3
Publisher site
See Article on Publisher Site

Abstract

This paper presents a hybrid approach for infinite impulse response (IIR) system identification, called ABC-PS, that combines artificial bee colony (ABC) and tissue P systems. A tissue P system with fully connected structure of cells has been considered as its computing framework. A modification of ABC was developed as evolution rules for objects according to fully connected structure and communication mechanism. With the control of the object’s evolution-communication mechanism, the tissue P system designed can effectively and efficiently identify the optimal filter coefficients for an IIR system. The performance of ABC-PS was compared with artificial bee colony and several other evolutionary algorithms. Simulation results show that ABC-PS is superior or comparable to the other algorithms for the employed examples and can be efficiently used for IIR system identification.

Journal

Neural Computing and ApplicationsSpringer Journals

Published: Feb 4, 2016

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off