Balanced the Trade-offs Problem of ANFIS using Particle Swarm Optimization

Rini, Dian Palupi; Shamsuddin, Siti Mariyam; Yuhaniz, Siti Sophiayati

doi:10.12928/telkomnika.v11i3.1146

Cited by 18 publications

(7 citation statements)

References 5 publications

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“…They developed ANFIS based prediction models for predicting electricity prices, wind power and customer satisfaction for a new product. Turki et al [16] and Rini et al [17] applied PSO alone for training both the premise and consequent parameters of ANFIS based models. Rini, Shamsuddin [17] also used PSO for ANFIS training.…”

Section: Training Methods Of Anfismentioning

confidence: 99%

“…Turki et al [16] and Rini et al [17] applied PSO alone for training both the premise and consequent parameters of ANFIS based models. Rini, Shamsuddin [17] also used PSO for ANFIS training. In addition to parameter identification, they also optimized fuzzy rule-base by applying threshold value on the rules' firing strength.…”

Section: Training Methods Of Anfismentioning

confidence: 99%

“…This raises the need of optimizing the rule-base. There is a little evidence of this matter as only Rini, Shamsuddin [17] and Wang, Zhang [25] are among the other researchers, mentioned in Table 2, have tried obtaining useful rule-set by pruning less-important rules. They tried to perform accuracy maximization and complexity minimization simultaneously in order to achieve ANFIS systems with high accuracy and interpretability.…”

Section: Training Methods Of Anfismentioning

confidence: 99%

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A Review of Training Methods of ANFIS for Applications in Business and Economics

Salleh¹,

Hussain²

2016

IJUNESST

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Fuzzy Neural Networks (FNNs) techniques have been effectively used in applications that range from medical to mechanical engineering, to business and economics. Despite of attracting researchers in recent years and outperforming other fuzzy systems, Adaptive Neuro-Fuzzy Inference System (ANFIS) still needs effective parameter training and rulebase optimization methods to perform efficiently when the number of inputs increase. Moreover, the standard gradient based learning via two pass learning algorithm is prone slow and prone to get stuck in local minima. Therefore many researchers have trained ANFIS parameters using metaheuristic algorithms however very few have considered optimizing the ANFIS rule-base. Mostly Particle Swarm Optimization (PSO) and its variants have been applied for training approaches used. Other than that, Genetic Algorithm (GA), Firefly Algorithm (FA), Ant Bee Colony (ABC) optimization methods have been employed for effective training of ANFIS networks when solving various problems in the field of business and finance.

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Section: Training Methods Of Anfismentioning

confidence: 99%

Section: Training Methods Of Anfismentioning

confidence: 99%

Section: Training Methods Of Anfismentioning

confidence: 99%

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A Review of Training Methods of ANFIS for Applications in Business and Economics

Salleh¹,

Hussain²

2016

IJUNESST

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“…Many researchers have proposed different approaches of training the ANFIS network utilizing optimization algorithms alone or in combination to gradient methods (Catalao et al 2011;Jiang et al 2012;Pousinho et al 2011Pousinho et al , 2012Turki et al 2012;Rini et al 2013). One of the algorithms used for training the ANFIS network is the Jaya optimization algorithm.…”

Section: Hybrid Model: Training the Anfis By The Jaya Optimization Almentioning

confidence: 99%

An intelligent integrated approach of Jaya optimization algorithm and neuro-fuzzy network to model the stratified three-phase flow of gas–oil–water

2019

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The problem of how to accurately measure the volume fractions of oil-gas-water mixtures in a pipeline remains as one of the key challenges in the petroleum industry. The current research highlights the capability of a hybrid system of the Jaya optimization algorithm and the adaptive neuro-fuzzy inference system (ANFIS), to model the stratified three-phase flow of gas-oil-water. As a matter of fact, the present study devotes to forecast the volume fractions in the stratified three-phase flow, on the basis of a dual-energy metering system, including the 152 Eu and 137 Cs and one NaI detector, using the aforementioned hybrid model. Since the summation of volume fractions are constant (equal to 100%), a constraint modelling problem exists, meaning that the hybrid model must forecast only two volume fractions. In this paper, three main hybrid models are employed. The first network is applied to forecast the gas and water volume fractions, the next one to forecast the water and oil volume fractions, and the last one to forecast the oil and gas volume fractions. For the next step, the hybrid models are trained based on numerically obtained data from the MCNP-X code.

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“…(c) Use of population-based algorithms to obtain interpretable systems (see e.g. [49,60,65,74]). (d) Use of possibilities of nonsupervising learning in the field of initialization of fuzzy rules for increasing interpretability (see e.g.…”

Section: Introductionmentioning

confidence: 99%

A New Interpretability Criteria for Neuro-Fuzzy Systems for Nonlinear Classification

Łapa

Cpałka

Galushkin

2015

Artificial Intelligence and Soft Computing

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Abstract. In this paper a new approach for construction of neuro-fuzzy systems for nonlinear classification is introduced. In particular, we concentrate on the flexible neuro-fuzzy systems which allow us to extend notation of rules with weights of fuzzy sets. The proposed approach uses possibilities of hybrid evolutionary algorithm and interpretability criteria of expert knowledge. These criteria include not only complexity of the system, but also semantics of the rules. The approach presented in our paper was tested on typical nonlinear classification simulation problems.

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Balanced the Trade-offs Problem of ANFIS using Particle Swarm Optimization

Abstract: Abstrak

Cited by 18 publications

References 5 publications

A Review of Training Methods of ANFIS for Applications in Business and Economics

A Review of Training Methods of ANFIS for Applications in Business and Economics

An intelligent integrated approach of Jaya optimization algorithm and neuro-fuzzy network to model the stratified three-phase flow of gas–oil–water

A New Interpretability Criteria for Neuro-Fuzzy Systems for Nonlinear Classification

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