On the exact solutions to some system of complex nonlinear models

Sulaıman, Tukur Abdulkadir; Bulut, Hasan; Başkonuş, Hacı Mehmet

doi:10.2478/amns.2020.2.00007

Cited by 78 publications

(38 citation statements)

References 67 publications

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“…A number of numerical formulation schemes have been used by the researcher's community to solve the system of nonlinear equations. Some of them are the differential transformation approach [11], Adams numerical approach [12], variational iteration method [13], Caputo fractional difference scheme [14], and many more [15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Competency of Neural Networks for the Numerical Treatment of Nonlinear Host-Vector-Predator Model

Sabir

Umar

Shah

et al. 2021

Computational and Mathematical Methods in Medicine

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The aim of this work is to introduce a stochastic solver based on the Levenberg-Marquardt backpropagation neural networks (LMBNNs) for the nonlinear host-vector-predator model. The nonlinear host-vector-predator model is dependent upon five classes, susceptible/infected populations of host plant, susceptible/infected vectors population, and population of predator. The numerical performances through the LMBNN solver are observed for three different types of the nonlinear host-vector-predator model using the authentication, testing, sample data, and training. The proportions of these data are chosen as a larger part, i.e., 80% for training and 10% for validation and testing, respectively. The nonlinear host-vector-predator model is numerically treated through the LMBNNs, and comparative investigations have been performed using the reference solutions. The obtained results of the model are presented using the LMBNNs to reduce the mean square error (MSE). For the competence, exactness, consistency, and efficacy of the LMBNNs, the numerical results using the proportional measures through the MSE, error histograms (EHs), and regression/correlation are performed.

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Section: Introductionmentioning

confidence: 99%

Competency of Neural Networks for the Numerical Treatment of Nonlinear Host-Vector-Predator Model

Sabir

Umar

Shah

et al. 2021

Computational and Mathematical Methods in Medicine

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“…Generalized trapezoidal numbers reduce the computational cost for a transportation problem using GITrFNs, as proposed by Ebrahimnejad [12]. Mathematical modeling and solutions to the models are important and their importance can be seen in management, social, and engineering applications [13][14][15][16][17][18][19][20][21]. Rashid, Beg, and Husnine [22] proposed an extended technique for order preference by similarity to the ideal solution (TOPSIS), which uses GITrFNs for the selection of robots considering both objective and subjective criteria but lacks a weights deriving procedure.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Among them, C 1 , C 2 and C 3 are subjective criteria and C 4 , C 5 and C 6 are objective criteria. Decision makers independently selected best and worst criteria by giving their preferences using linguistic variables from Table 1 that were recorded in Table 3 and their weights were calculated using GITrF-BWM (Model 11), and the calculated weights were aggregated using Equation (13) to have final aggregated weights of criteria recorded in Table 4.…”

Section: Optimal Robot Selection Processmentioning

confidence: 99%

Comparative Analysis of Hybrid Fuzzy MCGDM Methodologies for Optimal Robot Selection Process

et al. 2021

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The generalized interval-valued trapezoidal fuzzy best-worst method (GITrF-BWM) provides more reliable and more consistent criteria weights for multiple criteria group decision making (MCGDM) problems. In this study, GITrF-BWM is integrated with the extended TOPSIS (technique for order preference by similarity to the ideal solution) and extended VIKOR (visekriterijumska optimizacija i kompromisno resenje) methods for the selection of the optimal industrial robot using fuzzy information. For a criteria-based selection process, assigning weights play a vital role and significantly affect the decision. Assigning weights based on direct opinions of decision makers can be biased, so weight deriving models, such as GITrF-BWM, overcome this discrepancy. In previous studies, generalized interval-valued trapezoidal fuzzy weights were not derived by using any MCGDM method for the robot selection process. For this study, both subjective and objective criteria are considered. The preferences of decision makers are provided with the help of linguistic terms that are then converted into fuzzy information. The stability and reliability of the methods were tested by performing sensitivity analysis, which showed that the ranking results of both the methodologies are not symmetrical, and the integration of GITrF-BWM with the extended TOPSIS method provides stable and reliable results as compared to the integration of GITrF-BWM with the extended VIKOR method. Hence, the proposed methodology provides robust optimal industrial robot selection.

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“…The algorithm in this paper is mainly aimed at the periodic behaviour data of the human body collected by the wearable motion capture system [5]. We need to functionalise the discrete motion data to obtain the continuous characteristics of human motion.…”

Section: The Fitting Model and The Determination Of The Frequency Of The Fitting Functionmentioning

confidence: 99%

Mathematical function data model analysis and synthesis system based on short-term human movement

Xu¹,

Yang²,

Alghazzawi

et al. 2021

Applied Mathematics and Nonlinear Sciences

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The paper proposes a data model analysis algorithm for human motion function based on short-term behaviour. The algorithm uses a functional data analysis (FDA) method to perform Fourier fitting on the motion data and extract the fitted approximate single period data. Finally, the algorithm depicts the internal change in the motion in the low-dimensional space. The study found that the characteristic motion data obtained by the algorithm has smooth characteristics, and the relevant case analysis also verifies the algorithm's effectiveness.

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On the exact solutions to some system of complex nonlinear models

Cited by 78 publications

References 67 publications

Competency of Neural Networks for the Numerical Treatment of Nonlinear Host-Vector-Predator Model

Competency of Neural Networks for the Numerical Treatment of Nonlinear Host-Vector-Predator Model

Comparative Analysis of Hybrid Fuzzy MCGDM Methodologies for Optimal Robot Selection Process

Mathematical function data model analysis and synthesis system based on short-term human movement

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