Improving fuzzy knowledge integration with particle swarmoptimization

Huang, Angus F. M.; Yang, Stephen J. H.; Wang, Minhong; Tsai, Jeffrey J. P.

doi:10.1016/j.eswa.2010.06.030

Cited by 16 publications

(6 citation statements)

References 40 publications

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“…In their paper Huang et al [12] present the use of particle swarm optimisation in integrating many fuzzy information sources. Thank to this they obtain a knowledge base that would consist of correct rules and that would not be too complicated.…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic verification of a knowledge base by using a multi-criteria group evaluation with application to security screening at an airport

Skorupski

2015

Knowledge-Based Systems

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Section: Literature Reviewmentioning

confidence: 99%

Automatic verification of a knowledge base by using a multi-criteria group evaluation with application to security screening at an airport

Skorupski

2015

Knowledge-Based Systems

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“…QIEA maintains a population of real-coded triploid chromosome =      and      describes the corresponding fitness values of individuals where N is the size of population and is an individual defined in (10). A rule in contains six values, center (C) and width for each of three variables ( and ).…”

Section: C1 Representation and Evaluation Of Rule Base In Qieamentioning

confidence: 99%

Design and Implementation of an Effective Fuzzy Logic Controller based on Quantum Inspired Evolutionary Algorithm

Shill¹,

Hossain²,

Hossain³

et al. 2012

JCP

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This paper proposes a new approach based on quantum inspired evolutionary algorithm (QIEA) for effective selection and definition of fuzzy if-then control rules as well as the shapes of membership functions (MFs) to design fuzzy logic controllers (FLCs). The majority of works done on designing FLCs rely on the knowledge base derived from imprecise heuristic knowledge of experienced operators or persons. These traditional methods, however, are cumbersome to implement and very time consuming to evaluate. Our proposed approach is a self-learning adaptive method and decomposes a problem in such a way that leads to more effective knowledge acquisition and improved control performance with the FLCs. In order to verify the effectiveness of this self-learning adaptive method, a standard test-bed, the truck backer-upper problem, is considered as the test problem. During each generation, the rules are updated and the MFs’ parameters are altered using a complementary double mutation operator (CDMO) and a discrete crossover (DC). This paper also demonstrates the effect of different fuzzification and defuzzification<a href="http://www.actapress.com/Abstract.aspx?paperId=13588" target="_blank"></a> methods on the response of the FLC. The center of gravity (COG) and modified COG are used as defuzzifier to analyze the results of the fuzzy controller. The experimental results show that the proposed approach with different fuzzification and MCOG to design FLCs performs better than the traditional methods with triangular fuzzification and COG in terms of required time to backing up the truck

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“…They applied this method to pattern recognition. Huang [7] has presented a fuzzy knowledge integration technique based on the PSO. Their proposed approach consists of two phases: Firstly, it encodes the fuzzy rule sets and fuzzy sets with its corresponding MFs.…”

Section: Introductionmentioning

confidence: 99%

An adaptive fuzzy logic controller based on real coded quantum-inspired evolutionary algorithm

Shill

Hossain

Amin

et al. 2011

2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011)

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In this paper, fuzzy logic control systems and real coded quantum inspired evolutionary algorithm (RCQIEA) are integrated for intelligent control. Here, RCQIEAs is utilized as an adaptive method for selection and definition of fuzzy control rules and for tuning the parameters of membership function for each fuzzy control rule in two different ways. The majority of fuzzy logic controllers (FLCs) to date are working based on the expert knowledge base derived from heuristic knowledge of experienced operators. These approaches are difficult and time consuming for experts. Moreover, because manual coded FLCs use expert knowledge, there is no guarantee that the FLCs obtained will have sufficiently good performance, especially for a complex system problem with a large number of input variables. On the contrary, our proposed approach is an automatic knowledge acquisition learning method for generating or adapting FLCs using RCQIEA. In order to check the effectiveness of our proposed approach, it has been applied to solve the truck-and-trailer controller, which is well known testbed for fuzzy control systems. The fuzzy controller obtained by the proposed approach performs better and effectively realizes the trajectory control of the truck with trailer.

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Improving fuzzy knowledge integration with particle swarmoptimization

Cited by 16 publications

References 40 publications

Automatic verification of a knowledge base by using a multi-criteria group evaluation with application to security screening at an airport

Automatic verification of a knowledge base by using a multi-criteria group evaluation with application to security screening at an airport

Design and Implementation of an Effective Fuzzy Logic Controller based on Quantum Inspired Evolutionary Algorithm

An adaptive fuzzy logic controller based on real coded quantum-inspired evolutionary algorithm

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