Heuristic optimization of submerged hydrofoil using ANFIS–PSO

Djavareshkian, Mohammad Hassan; Esmaeili, Ali

doi:10.1016/j.oceaneng.2014.09.033

Cited by 20 publications

(12 citation statements)

References 32 publications

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“…potential of the particle swarm optimizer in tuning the membership function parameters of the ANFIS predictive model. is is in synchronization with various other applied engineering problems [70][71][72]. On the other aspect, the data preprocessing demonstrated an excellent procedure where to produce more meaningful information for the predictive model.…”

Section: Application Analysis and Discussionmentioning

confidence: 97%

Shallow Foundation Settlement Quantification: Application of Hybridized Adaptive Neuro‐Fuzzy Inference System Model

Mohammed

Sharafati

Al‐Ansari

et al. 2020

Advances in Civil Engineering

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Settlement simulating in cohesion materials is a crucial issue due to complexity of cohesion soil texture. This research emphasis on the implementation of newly developed machine learning models called hybridized Adaptive Neuro-Fuzzy Inference System (ANFIS) with Particle Swarm Optimization (PSO) algorithm, Ant Colony optimizer (ACO), Differential Evolution (DE), and Genetic Algorithm (GA) as efficient approaches to predict settlement of shallow foundation over cohesion soil properties. The width of footing (B), pressure of footing (qa), geometry of footing (L/B), count of SPT blow (N), and ratio of footing embedment (Df/B) are considered as predictive variables. Nonhomogeneity and inconsistency of employed dataset is a major concern during prediction modeling. Hence, two different modeling scenarios (i) preprocessed dataset (PP) and (ii) nonprocessed (initial) dataset (NP) were inspected. To assess the accuracy of the applied hybrid models and standalone one, multiple statistical metrics were computed and analyzed over the training and testing phases. Results indicated ANFIS-PSO model exhibited an accurate and reliable prediction data intelligent and had the highest predictability performance against all employed models. In addition, results demonstrated that data preprocessing is highly essential to be performed prior to building the predictive models. Overall, ANFIS-PSO model showed a robust machine learning for settlement prediction.

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Section: Application Analysis and Discussionmentioning

confidence: 97%

Shallow Foundation Settlement Quantification: Application of Hybridized Adaptive Neuro‐Fuzzy Inference System Model

Mohammed

Sharafati

Al‐Ansari

et al. 2020

Advances in Civil Engineering

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“…The hybrid model of the ANFIS-PSO (also known as PSO-ANFIS) appeared in the works of Catalao et al [79,80] in early 2011 for the prediction of wind energy and electricity pricing. Since then, this method has been used in various applications, e.g., load shedding, electricity prices forecasting, hydrofoil, travel time estimation, prediction of viscosity of mixed oils, matrix membranes modeling, wax deposition, electric power forecasting, asphaltene precipitation, prediction of the density of bitumen diluted with solvents, heating value of biomass, predicted interfacial tension of hydrocarbons and brine, prediction of gas density, forecasting oil flocculated asphaltene, biodiesel efficiency, biomass heating modeling, prediction of property damage, and solar radiation forecasting [81][82][83][84][85][86][87][88][89][90][91][92][93][94][95][96][97]. The ability to generalize, higher accuracy, speed, and ease of use have been reported as the main characteristics of the ANFIS-PSO.…”

Section: Literature Reviewmentioning

confidence: 99%

Developing an ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

et al. 2019

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Accurate prediction of mercury content emitted from fossil-fueled power stations is of the utmost importance for environmental pollution assessment and hazard mitigation. In this paper, mercury content in the output gas of power stations’ boilers was predicted using an adaptive neuro-fuzzy inference system (ANFIS) method integrated with particle swarm optimization (PSO). The input parameters of the model included coal characteristics and the operational parameters of the boilers. The dataset was collected from 82 sample points in power plants and employed to educate and examine the proposed model. To evaluate the performance of the proposed hybrid model of the ANFIS-PSO, the statistical meter of MARE% was implemented, which resulted in 0.003266 and 0.013272 for training and testing, respectively. Furthermore, relative errors between the acquired data and predicted values were between −0.25% and 0.1%, which confirm the accuracy of the model to deal non-linearity and represent the dependency of flue gas mercury content into the specifications of coal and the boiler type.

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“…The hybrid model of ANFIS-PSO (also known as PSO-ANFIS) has been appeared in the works of Catalao et al [79,80], in early 2011 for prediction of wind energy and electricity pricing prediction. Since then this method has been used in various applications, e.g., load shedding, electricity prices forecasting, hydrofoil, travel time estimation, prediction of viscosity of mixed oils, matrix membranes modeling, wax deposition, electric power forecasting, asphaltene precipitation, prediction of density of bitumen diluted with solvents, heating value of biomass, predict interfacial tension of hydrocarbons and brine, prediction of gas density, forecasting oil flocculated asphaltene, biodiesel efficiency, Biomass higher heating modeling, prediction of property damage, and solar radiation forecasting [81][82][83][84][85][86][87][88][89][90][91][92][93][94][95][96][97]. The generalization ability, higher accuracy, speed, and ease of use have been reported as the main characteristics of ANFIS-PSO.…”

Section: Previous Investigationsmentioning

confidence: 99%

Developing ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

Shamshirband¹,

Hadipoor²,

Baghban³

et al. 2019

Preprint

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Accurate prediction of mercury content emitted from fossil-fueled power stations is of utmost importance for environmental pollution assessment and hazard mitigation. In this paper, mercury content in the output gas of power stations' boilers was predicted using an adaptive neuro-fuzzy inference system (ANFIS) method integrated with particle swarm optimization (PSO). The input parameters of the model include coal characteristics and the operational parameters of the boilers. The dataset has been collected from 82 power plants and employed to educate and examine the proposed model. To evaluate the performance of the proposed hybrid model of ANFIS-PSO model, the statistical meter of MARE% was implemented, which resulted in 0.003266 and 0.013272 for training and testing, respectively.Furthermore, relative errors between acquired data and predicted values were between -0.25% and 0.1%, which confirm the accuracy of the model to deal nonlinearity and representing the dependency of flue gas mercury content into the specifications of coal and the boiler type.

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Heuristic optimization of submerged hydrofoil using ANFIS–PSO

Cited by 20 publications

References 32 publications

Shallow Foundation Settlement Quantification: Application of Hybridized Adaptive Neuro‐Fuzzy Inference System Model

Shallow Foundation Settlement Quantification: Application of Hybridized Adaptive Neuro‐Fuzzy Inference System Model

Developing an ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

Developing ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

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