Robust Pareto design of ANFIS networks for nonlinear systems with probabilistic uncertainties

Jamali, Ali; Nariman-Zadeh, N.; Ashraf, Hafiz Muhammad; Jamali, Zohre

doi:10.1109/inista.2011.5946080

Cited by 6 publications

(7 citation statements)

References 17 publications

(19 reference statements)

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“…Therefore, data training must be validated before the model training phase. This study applies Monte Carlo method with probabilistic distribution for data acquisition, and studies have also proved that the approach is valid for the data training acquisition phase [61], [62], [83]. Moreover, with the ANFIS model evaluation mentioned earlier, the ANFIS model with subtractive clustering was found as the fittest model to aggregate the overall supply chain sustainability performance, as proposed.…”

Section: Anfis Topology and Simulationsmentioning

confidence: 99%

An Adaptive Fuzzy Multi-Criteria Model for Sustainability Assessment of Sugarcane Agroindustry Supply Chain

et al. 2022

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Supply chain sustainability assessment is key to maintaining and improving the performance of agroindustry supply chain, particularly in agroindustry sustainable development. Assessment of the agroindustry supply chain performance is a complex and dynamic process. Hence, there is a need for an adaptive fuzzy multi-criteria sustainability assessment model as an alternative method for analysis and improvement. This study aims to design an adaptive fuzzy multi-criteria sustainability assessment and improvement model of the sugarcane agroindustry supply chain. In this study, (1) fuzzy inference system (FIS) was developed to assess the performance of sustainability dimensions. This study proposed 24 indicators of 4 dimensions, namely, economic, social, environment, and resource. (2) Adaptive neuro fuzzy inference system (ANFIS) was designed for aggregating the overall supply chain sustainability performance. (3) The proposed fuzzy multi-criteria assessment model was compared with the common multidimensional scaling (MDS) and linear models. This study proved that the proposed synthesis of the FIS and ANFIS models was powerful and adaptive for evaluating supply chain sustainability and providing accurate results. (4) The strategy to improve sustainability performance was developed using the cosine amplitude method (CAM). The proposed model determined that the overall supply chain sustainability value was 68.58%, which is almost sustainable. Several strategies have been suggested to improve sustainability performance, including maintaining sugarcane supply by strengthening the partnership program and improving the mill's overall recovery, followed by factory revitalization or new factory investment.

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Section: Anfis Topology and Simulationsmentioning

confidence: 99%

An Adaptive Fuzzy Multi-Criteria Model for Sustainability Assessment of Sugarcane Agroindustry Supply Chain

et al. 2022

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“…The objective of the GMDH approach is to ascertain the unspecified parameters, denoted as a i , within the Volterra series. These a i parameters are computed for every pairing of input variables x i and x j inputs using regression methodologies. , Building upon this principle and accounting for the notion of least-squares error, the G function can be formulated as follows for a given set of M observations involving multi-input and single-output data pairs: , E = ∑ i = 1 M false( y i = G i normalO false) 2 M y i = f ( x i 1 , x i 2 , ... , x italicim ) , i = 1 , 2 , ... , m …”

Section: Model Developmentmentioning

confidence: 99%

“…These a i parameters are computed for every pairing of input variables x i and x j inputs using regression methodologies. 84 , 85 Building upon this principle and accounting for the notion of least-squares error, the G function can be formulated as follows for a given set of M observations involving multi-input and single-output data pairs: 86 , 87 …”

Section: Model Developmentmentioning

confidence: 99%

Toward Predicting Interfacial Tension of Impure and Pure CO₂-Brine Systems Using Robust Correlative Approaches

Wei-yu,

Sun,

Zhou

et al. 2024

ACS Omega

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In the context of global climate change, significant attention is being directed toward renewable energy and the pivotal role of carbon capture and storage (CCS) technologies. These innovations involve secure CO 2 storage in deep saline aquifers through structural and capillary processes, with the interfacial tension (IFT) of the CO 2 -brine system influencing the storage capacity of formations. In this study, an extensive data set of 2811 experimental data points was compiled to model the IFT of impure and pure CO 2 -brine systems. Three white-box machine learning (ML) methods, namely, genetic programming (GP), gene expression programming (GEP), and group method of data handling (GMDH) were employed to establish accurate mathematical correlations. Notably, the study utilized two distinct modeling approaches: one focused on impurity compositions and the other incorporating a pseudocritical temperature variable (Tcm) offering a versatile predictive tool suitable for various gas mixtures. Among the correlation methods explored, GMDH, employing five inputs, exhibited exceptional accuracy and reliability across all metrics. Its mean absolute percentage error (MAPE) values for testing, training, and complete data sets stood at 7.63, 7.31, and 7.38%, respectively. In the case of six-input models, the GEP correlation displayed the highest precision, with MAPE values of 9.30, 8.06, and 8.31% for the testing, training, and total data sets, respectively. The sensitivity and trend analyses revealed that pressure exerted the most significant impact on the IFT of CO 2 -brine, showcasing an adverse effect. Moreover, an impurity possessing a critical temperature below that of CO 2 resulted in an elevated IFT. Consequently, this relationship leads to higher impurity concentrations aligning with lower Tcm values and subsequently elevated IFT. Also, monovalent and divalent cation molalities exhibited a growing influence on the IFT, with divalent cations exerting approximately double the influence of monovalent cations. Finally, the Leverage approach confirmed both the reliability of the experimental data and the robust statistical validity of the best correlations established in this study.

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“…A multi-objective uniform diversity genetic algorithm (MUGA) has been presented in (Jamali et al, 2010;Jamali, Nariman-zadeh, Ashraf, & Jamali, 2011;Jamali et al, 2008) which will also be used in this work to optimally design the parameters of GMDH model. Structural parameters of Pareto genetic design of GMDHtype neural network are presented at Table 2.…”

Section: Pareto Optimal Design Of Gmdh Type-neural Networkmentioning

confidence: 99%

Modeling and Optimization of Poly Electrolyte Dosage in Water Treatment Process by GMDH Type- NN and MOGA

Akbari-Zadeh

Daghbandan

Yaghoobi

2013

International Journal of Chemoinformatics and Chemical Engineering

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Coagulation-flocculation is the most important parts of water treatment process. Traditionally, optimum pre coagulant dosage is determined by used jar tests in laboratory. However; jar tests are time-consuming, expensive, and less adaptive to changes in raw water quality in real time. Soft computing can be used to overcome these limitations. In this paper, multi-objective evolutionary Pareto optimal design of GMDH Type-Neural Network has been used for modeling and predicting of optimum poly electrolyte dosage in Rasht WTP, Guilan, Iran, using Input -output data sets. In this way, multi-objective uniform-diversity genetic algorithms (MUGA) are then used for Pareto optimization of GMDH networks. In order to achieve this modeling, the experimental data were divided into train and test sections. The predicted values were compared with those of experimental values in order to estimate the performance of the GMDH network. Also, Multi Objective Genetic Algorithms (MOGA) are then used for optimization of influence parameters in pre coagulant (Poly electrolyte) dosage.

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Robust Pareto design of ANFIS networks for nonlinear systems with probabilistic uncertainties

Cited by 6 publications

References 17 publications

An Adaptive Fuzzy Multi-Criteria Model for Sustainability Assessment of Sugarcane Agroindustry Supply Chain

An Adaptive Fuzzy Multi-Criteria Model for Sustainability Assessment of Sugarcane Agroindustry Supply Chain

Toward Predicting Interfacial Tension of Impure and Pure CO₂-Brine Systems Using Robust Correlative Approaches

Modeling and Optimization of Poly Electrolyte Dosage in Water Treatment Process by GMDH Type- NN and MOGA

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Robust Pareto design of ANFIS networks for nonlinear systems with probabilistic uncertainties

Cited by 6 publications

References 17 publications

An Adaptive Fuzzy Multi-Criteria Model for Sustainability Assessment of Sugarcane Agroindustry Supply Chain

An Adaptive Fuzzy Multi-Criteria Model for Sustainability Assessment of Sugarcane Agroindustry Supply Chain

Toward Predicting Interfacial Tension of Impure and Pure CO2-Brine Systems Using Robust Correlative Approaches

Modeling and Optimization of Poly Electrolyte Dosage in Water Treatment Process by GMDH Type- NN and MOGA

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Toward Predicting Interfacial Tension of Impure and Pure CO₂-Brine Systems Using Robust Correlative Approaches