Multi-objective optimum design of ANFIS for modelling and prediction of deformation of thin plates subjected to hydrodynamic impact loading

Jamali, Ali; Babaei, Hashem; Nariman-Zadeh, N.; Talesh, SH Ashraf; Mostofi, Tohid Mirzababaie

doi:10.1177/1464420716660332

Cited by 7 publications

(5 citation statements)

References 38 publications

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“…The data set instance 151 has two inputs and is generated with a supposed target from one of the inputs. (10) The square root of the MSE is the RMSE. Because the error metric has the same units as the dependent variable, it is frequently employed to improve its readability.…”

Section: Fig 5 the Decision Variables Or Vectorsmentioning

confidence: 99%

Estimation single output with a hybrid of ANFIS and MOPSO_HS

Yelghi

2024

Sakarya University Journal of Computer and Information Sciences

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In the field of soft computing, the Adaptive Neuro-Fuzzy Inference System (ANFIS) has been more well-liked in recent years for its predictive capabilities. Appropriate ANFIS parameter adjusting is critical, which creates a gap in its predictive integration with traditional optimization techniques. Although some academics have concentrated on incorporating single-objective optimization, they frequently encounter issues with reliability and stability when striving to solve problems. In this work, an innovative multi-objective optimization technique that integrates ANFIS with MOPSO_HS is introduced. The model has consistency in problem solving and shows accurate predictions for both odd and even interval input models. In addition, three actual datasets are used to demonstrate the effectiveness of the suggested model's integration. A comparison is made between the suggested integrated model and established algorithms after 20 runs of analysis. The algorithm's accuracy, stability, and dependability in resolving integration problems are demonstrated by the results, which also show how superior it is to alternative approaches.

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Section: Fig 5 the Decision Variables Or Vectorsmentioning

confidence: 99%

Estimation single output with a hybrid of ANFIS and MOPSO_HS

Yelghi

2024

Sakarya University Journal of Computer and Information Sciences

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“…For solving the equation, singular value decomposition approach is used. [29][30][31][32][33][34][35] Acquiring the unknown coefficients required to transform the experimental data into a dimensionless analysis. A simple model for the center of mass deflection of triangular clamped plates subjected to drop hammer is derived by using SVD.…”

Section: Nondimensional Analysismentioning

confidence: 99%

Experimental and empirical study of large transverse deformation of triangular plates subjected to water hammer shock loading

Haghgoo¹,

Rezasefat²,

Mahmoudi³

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part L:

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In this paper, the dynamic plastic response of isosceles triangular plates under hydrodynamic loads was investigated experimentally using a drop hammer machine. To do this, a series of experimental tests were carried out on mild steel triangular plates with different thicknesses to bring insight into the effect of geometry and mechanical properties of the plate on the deformation of specimens, which were impacted by a piston-induced pressure wave inside a water tube. The effects of various impact loads originated from different drop hammer standoff distances, and hammer weights concerning variations of deflection of the center of mass were described. The experimental results were presented in terms of deflection of the center of mass of the plates and deflection profiles. The experimental results showed that the plate with lower thickness experienced higher deflection-to-thickness ratio. An empirical model was also proposed based on new dimensionless numbers for triangular plates in order to predict the ratio of deflection of the center of mass to thickness. The dimensionless numbers considered the effects of plate geometry, hydrodynamic applied load, and mechanical properties of materials. Comparison between the experimental results and empirical predictions demonstrated that the suggested model is accurate enough to predict of the response of isosceles triangular plates under hydrodynamic loads.

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“…3 The structure of ANFIS network ANFIS network is a data learning algorithm that uses the fuzzy logic system to transform the given inputs into a desired output via highly interconnected artificial neural network processing elements and information connections, which it is weighted to map the numerical inputs into output. The structure of ANFIS network is based on Takagi-Sugeno fuzzy inference system, thus fuzzy inference system is described by a set of fuzzy IF-THEN rules that have adapted via learning algorithm of neural network [21][22][23][24][25]. The fuzzy rules are presented based on zero order Takagi-Sugeno model as in Eq.…”

Section: Ac/dc/ac Convertersmentioning

confidence: 99%

“…This paper proposes a novel controllable CBFT protection technique driven by ANFIS for studied DFIG wind turbines. The ANFIS is a type of artificial intelligent algorithms that involves the two algorithms of fuzzy logic system and neural network system [21][22][23]. Moreover, the ANFIS can be designed and employed to get the benefits of both fuzzy logic and neural structure in one scheme [24,25].…”

Section: Introductionmentioning

confidence: 99%

A novel controllable crowbar based on fault type protection technique for DFIG wind energy conversion system using adaptive neuro-fuzzy inference system

Noureldeen

Hamdan

2018

Prot Control Mod Power Syst

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This paper proposes a novel controllable crowbar based on fault type (CBFT) protection technique for doubly fed induction generator (DFIG) wind energy conversion system connected to grid. The studied system consists of six DFIG wind turbines with a capacity of 1.5 MW for each of them. The operation mechanism of proposed technique is used to connect a set of crowbar resistors in different connection ways via activation of controllable circuit breakers (CBs) depending on the detected fault type. For each phase of DFIG, a crowbar resistor is connected in parallel with a controllable CB and all of them are connected in series to grid terminals. The adaptive neuro-fuzzy inference system (ANFIS) networks are designed to detect the fault occurrence, classify the fault type, activate the CBs for crowbar resistors associated with faulted phases during fault period, and deactivate them after fault clearance. The effectiveness of proposed CBFT protection technique is investigated for different fault types such as symmetrical and unsymmetrical faults taking into account the single-phase to ground fault is the most frequently fault type that occurs in power systems. Also, a comparison between the behaviours of studied system in cases of using traditional parallel rotor crowbar, classical outer crowbar, and proposed CBFT protection techniques is studied. The fluctuations of DC-link voltage, active power, and reactive power for studied system equipped with different protection techniques are investigated. Moreover, the impacts of different crowbar resistance values on the accuracy of proposed technique are studied. The simulation results show that, the proposed technique enhances the stability of studied wind turbine generators and contributes in protection of their components during faults.

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Multi-objective optimum design of ANFIS for modelling and prediction of deformation of thin plates subjected to hydrodynamic impact loading

Cited by 7 publications

References 38 publications

Estimation single output with a hybrid of ANFIS and MOPSO_HS

Estimation single output with a hybrid of ANFIS and MOPSO_HS

Experimental and empirical study of large transverse deformation of triangular plates subjected to water hammer shock loading

A novel controllable crowbar based on fault type protection technique for DFIG wind energy conversion system using adaptive neuro-fuzzy inference system

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