Extracting TSK-type Neuro-Fuzzy model using the Hunting search algorithm

Bouzaida, Sana; Sakly, Anis; M’Śahli, Faouzi

doi:10.1080/03081079.2013.848355

Cited by 11 publications

(7 citation statements)

References 15 publications

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“…According to Table 2 , CS method gives the best results in terms of a number of rules (mean) fewer than the HUS method in [ 18 ], MSE (mean), and standard deviation (Std) in both training and testing stages compared with other methods. Also, the CS method gives good performances with the smaller number of evaluations than the result in [ 19 ].…”

Section: Application Examples and Discussionmentioning

confidence: 99%

“…The algorithm is based on approaching the leader having the best position in the group and reorganizing if the hunters are close to each other but still cannot find the optimum solution [18]. …”

Section: Metaheuristic Algorithmsmentioning

confidence: 99%

“…The hunting search (HUS) algorithm imitates the social behavior of animals when catching a prey in the way wolves hunt. The algorithm is based on approaching the leader having the best position in the group and reorganizing if the hunters are close to each other but still cannot find the optimum solution [ 18 ].…”

Section: Metaheuristic Algorithmsmentioning

confidence: 99%

“…Other metaheuristic named hunting search (HUS) is used in [ 18 ] to determine the parameters of the T–S fuzzy model.…”

Section: Introductionmentioning

confidence: 99%

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Extracting T–S Fuzzy Models Using the Cuckoo Search Algorithm

Turki

Sakly

2017

Computational Intelligence and Neuroscience

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A new method called cuckoo search (CS) is used to extract and learn the Takagi–Sugeno (T–S) fuzzy model. In the proposed method, the particle or cuckoo of CS is formed by the structure of rules in terms of number and selected rules, the antecedent, and consequent parameters of the T–S fuzzy model. These parameters are learned simultaneously. The optimized T–S fuzzy model is validated by using three examples: the first a nonlinear plant modelling problem, the second a Box–Jenkins nonlinear system identification problem, and the third identification of nonlinear system, comparing the obtained results with other existing results of other methods. The proposed CS method gives an optimal T–S fuzzy model with fewer numbers of rules.

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Section: Application Examples and Discussionmentioning

confidence: 99%

Section: Metaheuristic Algorithmsmentioning

confidence: 99%

Section: Metaheuristic Algorithmsmentioning

confidence: 99%

“…Other metaheuristic named hunting search (HUS) is used in [ 18 ] to determine the parameters of the T–S fuzzy model.…”

Section: Introductionmentioning

confidence: 99%

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Extracting T–S Fuzzy Models Using the Cuckoo Search Algorithm

Turki

Sakly

2017

Computational Intelligence and Neuroscience

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“…Pioneer of them can be given as Ajit Abraham [13] Jang et al [14], Mendel [15] and Wu & Mendal [16], Emilio SoriaOlivas et al [17], Ching Long Su et al [18], John & Coupland [19], Oscar Castillo & Patricia Melin [20]. Much application specific work is done by various researchers including Malkawi & Murad [21], Nie et al [22], Bouzaidaa et al [23] and Azriyenni & Mustafa [24].…”

Section: Hybrid Systemsmentioning

confidence: 99%

Computer Aided Development of Fuzzy, Neural and Neuro-Fuzzy Systems

Sajja¹

2017

IJEACS

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Abstract-Development of an expert system is difficult because of two challenges involve in it. The first one is the expert system itself is high level system and deals with knowledge, which make is difficult to handle. Second, the systems development is more art and less science; hence there are little guidelines available about the development. This paper describes computer aided development of intelligent systems using modem artificial intelligence technology. The paper illustrates a design of a reusable generic framework to support friendly development of fuzzy, neural network and hybrid systems such as neuro-fuzzy system. The reusable component libraries for fuzzy logic based systems, neural network based system and hybrid system such as neuro-fuzzy system are developed and accommodated in this framework. The paper demonstrates code snippets, interface screens and class libraries overview with necessary technical details.

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