Conventional fuzzy logic controller is applicable when there are only
two fuzzy inputs with usually one output. Complexity increases when
there are more than one inputs and outputs making the system unrealizable.
The ordinal structure model of fuzzy reasoning has an advantage of
an easier approach of setting the rules with multiple inputs and
outputs. This is achieved by giving an associated weight to each
rule in the defuzzification process. An ordinal fuzzy logic controller
has been designed with application for obstacle avoidance of Khepera
mobile robot. Implementation show that ordinal structure fuzzy is
easier to design compared to conventional fuzzy controller. However
finding the best weight for each rule is a large and complex search
problem. A specially tailored Genetic Algorithm (GA) approach has
been proposed to find the best weight value for each rule in the
ordinal structure fuzzy controller. In this work, the comparison
of direct and incremental GA for optimization of the controller is
presented. Simulation results demonstrated significantly improved
obstacle avoidance performance of incremental GA optimization of
ordinal fuzzy controllers compared to direct GA optimized controller.
%0 Generic
%1 kmbs_osm2008a
%A Khairulmizam Samsudin,
%A Ahmad, Faisul Arif
%A Mashohor, Syamsiah
%A Latif, Norfadzilah Mohd
%B Ninth ACIS International Conference on Software Engineering, Artificial
Intelligence, Networking, and Parallel/Distributed Computing (SNPD2008)
%C Phuket, Thailand
%D 2008
%I IEEE Computer Society
%K imported
%P 128-134
%R 10.1109/SNPD.2008.70
%T Comparison of Direct and Incremental Genetic Algorithm for Optimization
of Ordinal Fuzzy Controllers
%U http://ieeexplore.ieee.org/iel5/4617324/4617326/04617360.pdf?tp=&arnumber=4617360&isnumber=4617326
%X Conventional fuzzy logic controller is applicable when there are only
two fuzzy inputs with usually one output. Complexity increases when
there are more than one inputs and outputs making the system unrealizable.
The ordinal structure model of fuzzy reasoning has an advantage of
an easier approach of setting the rules with multiple inputs and
outputs. This is achieved by giving an associated weight to each
rule in the defuzzification process. An ordinal fuzzy logic controller
has been designed with application for obstacle avoidance of Khepera
mobile robot. Implementation show that ordinal structure fuzzy is
easier to design compared to conventional fuzzy controller. However
finding the best weight for each rule is a large and complex search
problem. A specially tailored Genetic Algorithm (GA) approach has
been proposed to find the best weight value for each rule in the
ordinal structure fuzzy controller. In this work, the comparison
of direct and incremental GA for optimization of the controller is
presented. Simulation results demonstrated significantly improved
obstacle avoidance performance of incremental GA optimization of
ordinal fuzzy controllers compared to direct GA optimized controller.
@conference{kmbs_osm2008a,
abstract = {Conventional fuzzy logic controller is applicable when there are only
two fuzzy inputs with usually one output. Complexity increases when
there are more than one inputs and outputs making the system unrealizable.
The ordinal structure model of fuzzy reasoning has an advantage of
an easier approach of setting the rules with multiple inputs and
outputs. This is achieved by giving an associated weight to each
rule in the defuzzification process. An ordinal fuzzy logic controller
has been designed with application for obstacle avoidance of Khepera
mobile robot. Implementation show that ordinal structure fuzzy is
easier to design compared to conventional fuzzy controller. However
finding the best weight for each rule is a large and complex search
problem. A specially tailored Genetic Algorithm (GA) approach has
been proposed to find the best weight value for each rule in the
ordinal structure fuzzy controller. In this work, the comparison
of direct and incremental GA for optimization of the controller is
presented. Simulation results demonstrated significantly improved
obstacle avoidance performance of incremental GA optimization of
ordinal fuzzy controllers compared to direct GA optimized controller.},
added-at = {2008-09-17T01:02:13.000+0200},
address = {Phuket, Thailand},
author = {{\bf Khairulmizam Samsudin} and Ahmad, Faisul Arif and Mashohor, Syamsiah and Latif, Norfadzilah Mohd},
biburl = {https://www.bibsonomy.org/bibtex/26ce83148641311ffea8a9892083f352c/kmbs},
booktitle = {Ninth ACIS International Conference on Software Engineering, Artificial
Intelligence, Networking, and Parallel/Distributed Computing (SNPD2008)},
doi = {10.1109/SNPD.2008.70},
interhash = {9f7a4221346fb47135560b8f6e458f6c},
intrahash = {6ce83148641311ffea8a9892083f352c},
keywords = {imported},
month = {August},
owner = {kmbs},
pages = {128-134},
publisher = {IEEE Computer Society},
timestamp = {2008-09-17T01:02:13.000+0200},
title = {Comparison of Direct and Incremental {Genetic Algorithm} for Optimization
of {Ordinal Fuzzy} Controllers},
url = {http://ieeexplore.ieee.org/iel5/4617324/4617326/04617360.pdf?tp=&arnumber=4617360&isnumber=4617326},
year = 2008
}