Chen, Jun and Mahfouf, M.
(2008)
An immune algorithm based fuzzy predictive modeling mechanism using variable length coding and multi-objective optimization allied to engineering materials processing.
In: Granular Computing, 2008. GrC 2008. IEEE International Conference on, 26-28 August 2008, Hangzhou, China.
Full content URL: http://dx.doi.org/10.1109/GRC.2008.4664729
| An immune algorithm based fuzzy predictive modeling mechanism using variable length coding and multi-objective optimization allied to engineering materials processing | | In this paper, a systematic multi-objective fuzzy modeling approach is proposed, which can be regarded as a three-stage modeling procedure. In the first stage, an evolutionary based clustering algorithm is developed to extract an initial fuzzy rule base from the data. Based on this model, a back-propagation algorithm with momentum terms is used to refine the initial fuzzy model. The refined model is then used to seed the initial population of an immune inspired multi-objective optimization algorithm in the third stage to obtain a set of fuzzy models with improved transparency. To tackle the problem of simultaneously optimizing the structure and parameters, a variable length coding scheme is adopted to improve the efficiency of the search. The proposed modeling approach is applied to a real data set from the steel industry. Results show that the proposed approach is capable of eliciting not only accurate but also transparent fuzzy models. | | ![[img]](http://eprints.lincoln.ac.uk/style/images/fileicons/application_pdf.png)
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| Item Type: | Conference or Workshop contribution (Presentation) |
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| Item Status: | Live Archive |
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Abstract
In this paper, a systematic multi-objective fuzzy
modeling approach is proposed, which can be regarded
as a three-stage modeling procedure. In the first stage, an
evolutionary based clustering algorithm is developed to
extract an initial fuzzy rule base from the data. Based on
this model, a back-propagation algorithm with momentum
terms is used to refine the initial fuzzy model. The refined
model is then used to seed the initial population of an
immune inspired multi-objective optimization algorithm
in the third stage to obtain a set of fuzzy models with
improved transparency. To tackle the problem of
simultaneously optimizing the structure and parameters, a
variable length coding scheme is adopted to improve the
efficiency of the search. The proposed modeling approach
is applied to a real data set from the steel industry.
Results show that the proposed approach is capable of
eliciting not only accurate but also transparent fuzzy
models.
| Additional Information: | In this paper, a systematic multi-objective fuzzy
modeling approach is proposed, which can be regarded
as a three-stage modeling procedure. In the first stage, an
evolutionary based clustering algorithm is developed to
extract an initial fuzzy rule base from the data. Based on
this model, a back-propagation algorithm with momentum
terms is used to refine the initial fuzzy model. The refined
model is then used to seed the initial population of an
immune inspired multi-objective optimization algorithm
in the third stage to obtain a set of fuzzy models with
improved transparency. To tackle the problem of
simultaneously optimizing the structure and parameters, a
variable length coding scheme is adopted to improve the
efficiency of the search. The proposed modeling approach
is applied to a real data set from the steel industry.
Results show that the proposed approach is capable of
eliciting not only accurate but also transparent fuzzy
models. |
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| Keywords: | artificial immune systems, fuzzy predictive modeling, multi objective optimisation |
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| Subjects: | G Mathematical and Computer Sciences > G700 Artificial Intelligence |
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| Divisions: | College of Science > School of Engineering |
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| ID Code: | 2803 |
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| Deposited On: | 07 Jul 2010 10:37 |
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