Identification of fuzzy systems using a continuous ant colony algorithm

Ходашинский, И. А.; Dudin, P. A.

doi:10.3103/s8756699012010086

Cited by 7 publications

(6 citation statements)

References 8 publications

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“…However, the usage of these rules individually allowed us to find the optimum values only of some testing objective functions from the plurality of the examined ones. Using the rule (2) we were able to get the optimum of the function 9  with the error not more than 0.03, and using the rule (3) it was possible to get the optima of the functions 3  , 4  , and 8  with the error not more than 0.05.…”

Section: Functionmentioning

confidence: 90%

“…The major part of the stochastic methods is based on the search for the optimal solution which imitates the behavior of the complicated physical, biological, or social systems consisting of a considerable number of intercommunicate homogeneous elements [1][2][3][4]. This article suggests drawing upon the cellular automaton, which is an object of discrete mathematics characterized by different symmetry properties, both in geometrical and algebraic meaning.…”

Section: Introductionmentioning

confidence: 99%

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The Algorithm of Continuous Optimization Based on the Modified Cellular Automaton

et al. 2016

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This article is devoted to the application of the cellular automata mathematical apparatus to the problem of continuous optimization. The cellular automaton with an objective function is introduced as a new modification of the classic cellular automaton. The algorithm of continuous optimization, which is based on dynamics of the cellular automaton having the property of geometric symmetry, is obtained. The results of the simulation experiments with the obtained algorithm on standard test functions are provided, and a comparison between the analogs is shown.

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Section: Functionmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

The Algorithm of Continuous Optimization Based on the Modified Cellular Automaton

et al. 2016

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“…Although GD + LSE-based training algorithm has been widely applied, still its convergence efficiency is remained at below average. 28 In past few years, evolutionary-based global search approaches such as artificial bee colony (ABC), 29 PSO, 23 differential evolution (DE) 30 and ACO 31 have been employed as training algorithm for ANFIS network for eliminating drawbacks of BP algorithm. Such derivative-free global optimization can enhance the accuracy in parameter estimation.…”

Section: Learning Algorithms For Anfismentioning

confidence: 99%

Navigational strategy for underwater mobile robot based on adaptive neuro-fuzzy inference system model embedded with shuffled frog leaping algorithm–based hybrid learning approach

Parhi

Kundu

2017

Proceedings of the Institution of Mechanical Engineers, Part M:

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In this research article, a novel navigational approach has been introduced for underwater robot based on learning and self-adaptation ability of adaptive neuro-fuzzy inference system. For avoiding obstacles during three-dimensional navigation, two adaptive neuro-fuzzy inference system models have been coupled to find out required change in heading angles of underwater robot in horizontal and vertical planes, respectively. A new hybrid learning scheme has been proposed for adaptive neuro-fuzzy inference system. Here, memetic approach based shuffled frog leaping algorithm has been used to tune the premise parameters and consequent parameters has been estimated through recursive least square estimation. Minimization of error in output of adaptive neuro-fuzzy inference system model has been treated as major objective of evolutionary-based training algorithm. Preliminary robotic behaviors of underwater robot have been successfully executed by implementing such well-trained adaptive neuro-fuzzy inference system architecture within three-dimensional unspecified workspace. Navigational performance of adaptive neuro-fuzzy inference system trained with the proposed hybrid learning algorithm has been compared with other three-dimensional navigational approaches in simulation mode for authentication purpose. Experimental verification has also been carried out to validate the feasibility and efficiency of the proposed navigational strategy.

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“…Due to the complexity of the problem, many authors proposed the use metaheuristics, such as evolutionary algorithms (EAs) and swarm methods, to tackle the problem. Whilst EAs, particularly genetic algorithms, have been applied successfully to the optimization of FS [2,3], the application of swarm algorithms, such as ant colony algorithms [4,5] and particle swarm optimization (PSO) [6,7], is not so common. The focus of this paper is on the application of a variant of a PSO algorithm to address the problem of FS parameter tuning.…”

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confidence: 99%

Tuning fuzzy systems parameters with chaotic particle swarm optimization

Hodashinsky

Bardamova

2017

J. Phys.: Conf. Ser.

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Abstract. In this paper, we investigate the application of chaotic particle swarm optimization (PSO) to fuzzy system parameter estimation. Unlike traditional PSO, chaotic PSO improves search capabilities of particles using chaotic transformations of random coordinates. Different mapping functions were investigated to generate chaotic transformation sequences. The efficiency of the algorithm was tested on approximation problems originating from data sets of the KEEL repository. The experimental study compared several existing techniques for fuzzy systems identification. The results indicate that chaotic PSO is a competitive approach to fuzzy system identification able to create fuzzy systems of high precision at an acceptable set of rules.

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Identification of fuzzy systems using a continuous ant colony algorithm

Cited by 7 publications

References 8 publications

The Algorithm of Continuous Optimization Based on the Modified Cellular Automaton

The Algorithm of Continuous Optimization Based on the Modified Cellular Automaton

Navigational strategy for underwater mobile robot based on adaptive neuro-fuzzy inference system model embedded with shuffled frog leaping algorithm–based hybrid learning approach

Tuning fuzzy systems parameters with chaotic particle swarm optimization

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