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

Abstract

Drop hammer impact experiments have been carried out to assess the dynamic plastic response of fully clamped circular and rectangular plates made of aluminum and steel subjected to hydrodynamic impact loading at various energy levels. Also, the effective parameters in forming process are proposed in non-dimensional forms for modeling and prediction of the central deflection of plates using adaptive neuro-fuzzy inference system in conjunction with genetic algorithm and singular value decomposition method. Genetic algorithm is used for optimal scheme of Gaussian membership function’s variables and multi-objective Pareto optimal design of adaptive neuro-fuzzy inference system model. Also, the singular value decomposition method is applied to compute the linear parameters of the adaptive neuro-fuzzy inference system method. The important conflicting objectives of developed adaptive neuro-fuzzy inference system, namely, training error and prediction error, are obtained by dividing date sets into two parts. Hence, various optimal choices of adaptive neuro-fuzzy inference system model are provided which are non-dominated states from each other. Moreover, optimal Pareto front of such model leads to trade-off between the conflicting pair of considered objectives for two series of experiments. The results of this work indicate that multi-objective Pareto optimal design of adaptive neuro-fuzzy inference system predicts central deflection of plates with a good accuracy. In addition, the comparison between the adaptive neuro-fuzzy inference system model and exiting one demonstrates superior performance of the present approach in simulating central deflection of plates.

Estimation single output with a hybrid of ANFIS and MOPSO_HS

“…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.…”

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

“…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.…”

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

“…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.…”

“…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].…”