Assessment of human operator functional state using a novel differential evolution optimization based adaptive fuzzy model

Wang, Raofen; Zhang, Jianhua; Zhang, Yu; Wang, Xingyu

doi:10.1016/j.bspc.2011.09.004

Cited by 50 publications

(23 citation statements)

References 21 publications

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“…In the search of more effective training and optimization of ANFIS models, the researchers have explored the use of Firefly Algorithm (FA), Artificial Bee Colony (ABC), and Differential Evolution with Ant Colony Search (DEACS) algorithms [2], [24][25]. All of them were employed for both antecedent and consequent parameters learning.…”

Section: Training Methods Of Anfismentioning

confidence: 99%

“…All of them were employed for both antecedent and consequent parameters learning. Wang, Zhang [25] were among others who, together with parameter identification, also optimized the rule-base by pruning redundant rules through threshold value on rules' firing strength. Cat Swarm Optimization (CSO) algorithm has also been proposed by Orouskhani et al [26] in conjunction with GD to train MFs and linear coefficients, respectively.…”

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%

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

Salleh¹,

Hussain²

2016

IJUNESST

View full text Add to dashboard Cite

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“…Therefore, Ant Colony Search is used to search the suitable combination of , , and mutation strategies adaptively to accelerate the global search. Some researchers have found an inevitable relationship between the parameters ( , , and mutation strategies) and the optimization results of DE [16][17][18]. However, the approaches above are not applying the most suitable , , and mutation strategies simultaneously.…”

Section: Cultural Differential Evolution With Ant Searchmentioning

confidence: 99%

Steady Modeling for an Ammonia Synthesis Reactor Based on a Novel CDEAS-LS-SVM Model

Liu

Zhang

et al. 2014

Mathematical Problems in Engineering

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A steady-state mathematical model is built in order to represent plant behavior under stationary operating conditions. A novel modeling using LS-SVR based on Cultural Differential Evolution with Ant Search is proposed. LS-SVM is adopted to establish the model of the net value of ammonia. The modeling method has fast convergence speed and good global adaptability for identification of the ammonia synthesis process. The LS-SVR model was established using the above-mentioned method. Simulation results verify the validity of the method.

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“…With the fast development of EEG learning, we now can catch the EEG data quickly and analyze it in different ways, such as time domain and frequency domain analysis [2][3][4][5][6][7], neural network [7][8][9][10][11][12], chaotic analysis [13][14][15], etc. In this paper, we use multiscale symbolic transfer entropy (MSTE) to analyze the EEG with Lead Fp1 and Fp2.…”

Section: Introductionmentioning

confidence: 99%

Multiscale Symbolic Transfer Entropy (MSTE) Analysis in Closing Eyes and Being Idle

Du¹,

Yao²,

Wang³

2017

Proceedings of the 2017 5th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering (ICMMCCE 20

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Abstract. It's impressive to learn about how our minds work. In this work, we have discovered that when a person closes his or her eyes, the value of multiscale symbolic transfer entropy(MSTE) [1] of electroencephalogram (EEG, Lead Fp1 and Fp2) grows larger. In other words, the coupling of Lead Fp1 and Fp2 is more significant when the experimenter closes his or her eyes, and we can capture the dynamic information and the its change more easily, which is useful for BCI and other practical uses.

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Assessment of human operator functional state using a novel differential evolution optimization based adaptive fuzzy model

Cited by 50 publications

References 21 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

Steady Modeling for an Ammonia Synthesis Reactor Based on a Novel CDEAS-LS-SVM Model

Multiscale Symbolic Transfer Entropy (MSTE) Analysis in Closing Eyes and Being Idle

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