Applying ANN, ANFIS, and LSSVM Models for Estimation of Acid Solvent Solubility in Supercritical CO2

Bemani, Amin; Baghban, Alireza Akbarzadeh; Shamshirband, Shahaboddin; Mosavi, Amir; Csiba, Péter; Várkonyi-Kóczy, Annamária R.

doi:10.20944/preprints201906.0055.v1

Cited by 12 publications

(3 citation statements)

References 84 publications

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“…Finally, sensitivity analysis was used to determine the effect of different input parameters on the target parameter. More details about this analysis are given elsewhere [49,50]. According to Figure 5, it was found that T tot has the most direct effect on the target parameter, which corresponds to the relevancy factor (r) equal to +0.26, while other input parameters showed an inverse effect on the target parameter so that GLS showed the most negative effect with r equal to -0.85.…”

Section: Resultsmentioning

confidence: 99%

Implementing PSO-ELM Model to Approximate Trolox Equivalent Antioxidant Capacity as One of the Most Important Biological Properties of Food

Elveny

Akhmadeev

Dinari

et al. 2021

BioMed Research International

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In this paper, the Trolox equivalent antioxidant capacity (TEAC) is estimated through a robust machine-learning algorithm known as the Particle Swarm Optimization-based Extreme Learning Machine (PSO-ELM) model. For this purpose, a large dataset from previously published reports was gathered. Various analyses were performed to evaluate the proposed model. The results of the statistical analysis showed that this model can predict the actual values with high accuracy, so that the calculated R 2 and RMSE values were equal to 0.973 and 3.56, respectively. Sensitivity analysis was also performed on the effective input parameters. The leverage technique was also performed to check the accuracy of real data, and the results showed that the majority of data are reliable. This simple yet accurate model can be very powerful in predicting the Trolox equivalent antioxidant capacity values and can be a good alternative to laboratory data.

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Section: Resultsmentioning

confidence: 99%

Implementing PSO-ELM Model to Approximate Trolox Equivalent Antioxidant Capacity as One of the Most Important Biological Properties of Food

Elveny

Akhmadeev

Dinari

et al. 2021

BioMed Research International

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“…Therefore, the leaf flexible hinge is selected for the mechanism. To select appropriated dimensions for the mechanism, this investigation proposes a hybrid Taguchi approach based on grey relational analysis and neural network with fuzzy logic and ANFIS algorithms [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Optimum Design for the Magnification Mechanisms Employing Fuzzy Logic–ANFIS

Huynh¹,

Nguyen²,

Nguyen³

2022

Computers, Materials &Amp; Continua

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To achieve high work performance for compliant mechanisms of motion scope, continuous work condition, and high frequency, we propose a new hybrid algorithm that could be applied to multi-objective optimum design. In this investigation, we use the tools of finite element analysis (FEA) for a magnification mechanism to find out the effects of design variables on the magnification ratio of the mechanism and then select an optimal mechanism that could meet design requirements. A poly-algorithm including the Grey-Taguchi method, fuzzy logic system, and adaptive neuro-fuzzy inference system (ANFIS) algorithm, was utilized mainly in this study. The FEA outcomes indicated that design variables have significantly affected on magnification ratio of the mechanism and verified by analysis of variance and analysis of the signal to noise of grey relational grade. The results are also predicted by employing the tool of ANFIS in MATLAB. In conclusion, the optimal findings obtained: Its magnification is larger than 40 times in comparison with the initial design, the maximum principal stress is 127.89 MPa, and the first modal shape frequency obtained 397.45 Hz. Moreover, we found that the outcomes obtained deviation error compared with predicted results of displacement, stress, and frequency are 8.76%, 3.6%, and 6.92%, respectively.

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“…At the provincial level, the deep neural network assists in managing a lot of supplementary data [32]. For modeling and uncertainty analysis of groundwater levels, the adaptive neuro-fuzzy interface system with the grasshopper optimization algorithm and support vector machine exhibits the best and worst results, respectively [33]. The best methods for estimating the solubility of acids in supercritical carbon dioxide are provided by the radial basis function artificial neural network, multi-layer perceptron artificial neural network, least squares support vector machine, and adaptive neuro-fuzzy inference system.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Investigation into the Application of Convolutional Neural Networks (ConvNet/CNN) in Smart Grids

Rituraj

Ecker²

2022

SSRN Journal

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Applying ANN, ANFIS, and LSSVM Models for Estimation of Acid Solvent Solubility in Supercritical CO2

Cited by 12 publications

References 84 publications

Implementing PSO-ELM Model to Approximate Trolox Equivalent Antioxidant Capacity as One of the Most Important Biological Properties of Food

Implementing PSO-ELM Model to Approximate Trolox Equivalent Antioxidant Capacity as One of the Most Important Biological Properties of Food

Optimum Design for the Magnification Mechanisms Employing Fuzzy Logic–ANFIS

A Comprehensive Investigation into the Application of Convolutional Neural Networks (ConvNet/CNN) in Smart Grids

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