Pruning of Rule Base of a Neural Fuzzy Inference Network

Smarti Reel; Ashok Kumar Goel

doi:10.1007/978-3-642-22606-9_55

Pruning of Rule Base of a Neural Fuzzy Inference Network

Smarti Reel, Ashok Kumar Goel

Population Health and Genomics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this work, Neural Fuzzy Inference Network (NFIN) controller is implemented that has a number of membership functions and parameters that are tuned using Genetic Algorithms. The number of rules used to define the Neuro-Fuzzy controller is then pruned. Pruning is utilized effectively to eliminate irrelevant rules in the rule base, thus keeping only the relevant rules. Pruning is performed at various threshold levels without affecting the system performance. This methodology is implemented for Water Bath System and analysis has been carried out to investigate the effect of pruning using a multi-step reference input signal. From the results, it is concluded that reasonably good performance of controller can be obtained with lesser number of rules, thus, reducing the computational complexity of the network.

Original language	English
Title of host publication	Contemporary Computing
Subtitle of host publication	4th International Conference, IC3 2011, Noida, India, August 2011 Proceedings
Place of Publication	Berlin, Heidelberg
Publisher	Springer
Pages	541-542
Number of pages	2
ISBN (Electronic)	9783642226069
ISBN (Print)	9783642226052
DOIs	https://doi.org/10.1007/978-3-642-22606-9_55
Publication status	Published - Aug 2011
Event	4th International Conference on Contemporary Computing, IC3 2011 - Noida, India Duration: 8 Aug 2011 → 10 Aug 2011

Publication series

Name	Communications in Computer and Information Science (CCIS)
Publisher	Springer
Volume	168
ISSN (Print)	1865-0929

Conference

Conference	4th International Conference on Contemporary Computing, IC3 2011
Abbreviated title	IC3 2011
Country/Territory	India
City	Noida
Period	8/08/11 → 10/08/11

Keywords

artificial intelligence
fuzzy logic
Neural fuzzy inference network
neural network
pruning

ASJC Scopus subject areas

General Computer Science

Access to Document

10.1007/978-3-642-22606-9_55

Cite this

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title = "Pruning of Rule Base of a Neural Fuzzy Inference Network",

abstract = "In this work, Neural Fuzzy Inference Network (NFIN) controller is implemented that has a number of membership functions and parameters that are tuned using Genetic Algorithms. The number of rules used to define the Neuro-Fuzzy controller is then pruned. Pruning is utilized effectively to eliminate irrelevant rules in the rule base, thus keeping only the relevant rules. Pruning is performed at various threshold levels without affecting the system performance. This methodology is implemented for Water Bath System and analysis has been carried out to investigate the effect of pruning using a multi-step reference input signal. From the results, it is concluded that reasonably good performance of controller can be obtained with lesser number of rules, thus, reducing the computational complexity of the network.",

keywords = "artificial intelligence, fuzzy logic, Neural fuzzy inference network, neural network, pruning",

author = "Smarti Reel and Goel, {Ashok Kumar}",

year = "2011",

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series = "Communications in Computer and Information Science (CCIS)",

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Reel, S & Goel, AK 2011, Pruning of Rule Base of a Neural Fuzzy Inference Network. in Contemporary Computing : 4th International Conference, IC3 2011, Noida, India, August 2011 Proceedings. Communications in Computer and Information Science (CCIS), vol. 168 , Springer , Berlin, Heidelberg, pp. 541-542, 4th International Conference on Contemporary Computing, IC3 2011, Noida, India, 8/08/11. https://doi.org/10.1007/978-3-642-22606-9_55

Pruning of Rule Base of a Neural Fuzzy Inference Network. / Reel, Smarti; Goel, Ashok Kumar.
Contemporary Computing : 4th International Conference, IC3 2011, Noida, India, August 2011 Proceedings. Berlin, Heidelberg: Springer , 2011. p. 541-542 (Communications in Computer and Information Science (CCIS); Vol. 168 ).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Goel, Ashok Kumar

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N2 - In this work, Neural Fuzzy Inference Network (NFIN) controller is implemented that has a number of membership functions and parameters that are tuned using Genetic Algorithms. The number of rules used to define the Neuro-Fuzzy controller is then pruned. Pruning is utilized effectively to eliminate irrelevant rules in the rule base, thus keeping only the relevant rules. Pruning is performed at various threshold levels without affecting the system performance. This methodology is implemented for Water Bath System and analysis has been carried out to investigate the effect of pruning using a multi-step reference input signal. From the results, it is concluded that reasonably good performance of controller can be obtained with lesser number of rules, thus, reducing the computational complexity of the network.

AB - In this work, Neural Fuzzy Inference Network (NFIN) controller is implemented that has a number of membership functions and parameters that are tuned using Genetic Algorithms. The number of rules used to define the Neuro-Fuzzy controller is then pruned. Pruning is utilized effectively to eliminate irrelevant rules in the rule base, thus keeping only the relevant rules. Pruning is performed at various threshold levels without affecting the system performance. This methodology is implemented for Water Bath System and analysis has been carried out to investigate the effect of pruning using a multi-step reference input signal. From the results, it is concluded that reasonably good performance of controller can be obtained with lesser number of rules, thus, reducing the computational complexity of the network.

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KW - fuzzy logic

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Pruning of Rule Base of a Neural Fuzzy Inference Network

Abstract

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Conference

Keywords

ASJC Scopus subject areas

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