A fuzzy hierarchical operator in the grey wolf optimizer algorithm

Rodríguez, Luis; Castillo, Oscar; Soria, José Manuel Nieto; Melín, Patricia; Valdez, Fevrier; González, Claudia I.; Martínez, Gonzalo; Soto, Jesús

doi:10.1016/j.asoc.2017.03.048

Cited by 178 publications

(47 citation statements)

References 13 publications

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“…Finally, a fault-tolerant active control approach is proposed to combine output results. The importance of this research is from actual data of the TE process and complete simulation in MATLAB to detect four faults (which there is concurrency possibility for two of them) and five different working modes in the system as real-time by the fuzzy-neural fusion classifier and compare classification results with the presented sample in other reliable materials [18][19][20][21][22][23][24][25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

High efficiency fault-detection and fault-tolerant control approach in Tennessee Eastman process via fuzzy-based neural network representation

Adeli

Mazinan

2019

Complex Intell. Syst.

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We looked at the background of fault-detection and fault-tolerant control algorithms to propose a new high efficiency one with a focus on Tennessee Eastman process through fuzzy-based neural network representation. Due to the fact that the openloop system may not be stabilized, an advanced control strategy to generate proper control signals needs to be designed. At first, to detect and identify the fault, data preprocessing theories have been considered. Based upon the matter disclosed, to provide a reliable decision-maker block, fusion classifier idea has been realized. For this one, raw data, time, and frequency characteristics are divided into various classification tools and finally the obtained knowledge combination regarding each one of them is adopted. It should be noted that the proposed implementation tools are taken into real consideration as the fuzzy-based neural network representation. Subsequently, the fault-tolerant control approach based on local controller regulation in case of each fault occurrence has been researched, which the investigated outcomes emphasize the effectiveness of the approach proposed here.

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Section: Introductionmentioning

confidence: 99%

High efficiency fault-detection and fault-tolerant control approach in Tennessee Eastman process via fuzzy-based neural network representation

Adeli

Mazinan

2019

Complex Intell. Syst.

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“…In fact, this model is a single input and two output (SIMO) system. In this paper, the idea of the CNF controller to the inverted pendulum system and nonlinear magnetic ball suspension system has been extended by the SMC and GC algorithm [14][15][16][17]. The cuckoo search (CS) is a global random interactive search algorithm inspired by nature.…”

Section: Introductionmentioning

confidence: 99%

Incremental SMC-based CNF control strategy considering magnetic ball suspension and inverted pendulum systems through cuckoo search-genetic optimization algorithm

Mollabashi

Mazinan

2019

Complex Intell. Syst.

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A kind of incremental sliding mode control (SMC) approach in connection with the well-known composite nonlinear feedback (CNF) control strategy is newly considered in this research to deal with the nonlinear magnetic ball suspension and inverted pendulum systems, as well. The incremental SMC approach is in fact proposed to handle the aforementioned underactuated systems under control, which have a lower number of actuators than degrees of freedom. Based on the outcomes of the investigation presented here, the small overshoot and short settling time of the system response are fulfilled. In fact, the proposed CNF control strategy comprises two parts: the first term assures the stability of the closed-loop nonlinear system and provides a fast convergence response. The second term reduces its overshoot. The genetic-cuckoo hybrid algorithm is designed to minimize tracking errors for the purpose of finding the most suitable sliding surface coefficients. Finally, the finite time stability for the closed-loop system is proved, theoretically.Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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“…[30][31][32][33][34][35]. Moreover, many researchers have attempted different strategies to enhance the performance of GWO [36][37][38][39][40][41][42][43]. In this article, we will present a novel enhanced GWO optimization algorithm known as EOGWO by using a dynamic generalized opposition-based learning strategy (DGOBL).…”

Section: Introductionmentioning

confidence: 99%

A Novel Dynamic Generalized Opposition-Based Grey Wolf Optimization Algorithm

Xing

Wang

2018

Algorithms

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To enhance the convergence speed and calculation precision of the grey wolf optimization algorithm (GWO), this paper proposes a dynamic generalized opposition-based grey wolf optimization algorithm (DOGWO). A dynamic generalized opposition-based learning strategy enhances the diversity of search populations and increases the potential of finding better solutions which can accelerate the convergence speed, improve the calculation precision, and avoid local optima to some extent. Furthermore, 23 benchmark functions were employed to evaluate the DOGWO algorithm. Experimental results show that the proposed DOGWO algorithm could provide very competitive results compared with other analyzed algorithms, with a faster convergence speed, higher calculation precision, and stronger stability.

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A fuzzy hierarchical operator in the grey wolf optimizer algorithm

Cited by 178 publications

References 13 publications

High efficiency fault-detection and fault-tolerant control approach in Tennessee Eastman process via fuzzy-based neural network representation

High efficiency fault-detection and fault-tolerant control approach in Tennessee Eastman process via fuzzy-based neural network representation

Incremental SMC-based CNF control strategy considering magnetic ball suspension and inverted pendulum systems through cuckoo search-genetic optimization algorithm

A Novel Dynamic Generalized Opposition-Based Grey Wolf Optimization Algorithm

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