Modelling of multiple short-length-scale stall cells in an axial compressor using evolved GMDH neural networks

Amanifard, Nima; Nariman-Zadeh, N.; Farahani, Mehdi Hosseinabadi; Khalkhali, Abolfazl

doi:10.1016/j.enconman.2008.05.025

Cited by 86 publications

(24 citation statements)

References 18 publications

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“…Also, it is a series of operations of seeding, rearing, crossbreeding, selection and rejection of seeds correspond to the determination of the input variables, and structure and parameters of model, and selection of model by principle of termination (Madala and Ivakhnenko, 1994). The GMDH network is a very flexible algorithm and it can be hybridized by other evolutionary algorithms, such as genetic algorithm (Mehrara et al, 2009;Amanifard et al, 2008), genetic programming (Najafzadeh and Barani, 2011;Iba and de Garis, 1996), particle swarm optimization (Onwubolu, 2008), levenberg-marquardt (Najafzadeh et al, 2013c), and back propagations Azamathulla, 2013a, 2013b;Najafzadeh and Barani, 2011;Srinivasan, 2008;Sakaguchi and Yamamoto, 2000). Previous researches established that hybridizations were successful in finding solutions to problems in different fields.…”

Section: Group Methods Of Data Handling (Gmdh)mentioning

confidence: 97%

“…Among these methods, the GMDH network is known as a self-organized method to model and forecast the behaviors of unknown or complicated systems based on given input-output data pairs (Amanifard et al, 2008). In addition, the GMDH approach is used in different fields of engineering sciences such as energy conservation, control engineering, system identification, marketing, economic, and geology (Mehrara et al, 2009;Kalantary et al, 2009;Amanifard et al, 2008;Srinivasan, 2008;Witczak et al, 2006). In fact, the main advantage of the GMDH model is to build analytical functions within feed forward network based on quadratic polynomial whose weighting coefficients are obtained using regression method (Kalantary et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of GMDH networks for prediction of local scour depth at bridge abutments in coarse sediments with thinly armored beds

2015

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Section: Group Methods Of Data Handling (Gmdh)mentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Evaluation of GMDH networks for prediction of local scour depth at bridge abutments in coarse sediments with thinly armored beds

2015

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“…GMDH-NN is a machine learning algorithm based methodology that works by the principle of termination [49][50][51]. The principle of termination is a process where the data seeding, rearing, hybridizing, selection, and rejection of seeds relate to the determination of the input variables, structure, and parameters of the model.…”

Section: Group Methods Of Data Handling Neural Network (Gmdh-nn)mentioning

confidence: 99%

Anticipate Manning’s Coefficient in Meandering Compound Channels

2018

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Estimating Manning's roughness coefficient (n) is one of the essential factors in predicting the discharge in a stream. Present research work is focused on prediction of Manning's n in meandering compound channels by using the Group Method of Data Handling Neural Network (GMDH-NN) approach. The width ratio (α), relative depth (β), sinuosity (s), Channel bed slope (S o ), and meander belt width ratio (ω) are specified as input parameters for the development of the model. The performance of GMDH-NN is evaluated with two different machine learning techniques, namely the support vector regression (SVR) and multivariate adaptive regression spline (MARS) with various statistical measures. Results indicate that the proposed GMDH-NN model predicts the Manning's n satisfactorily as compared to the MARS and SVR model. This GMDH-NN approach can be useful for practical implementation as the prediction of Manning's coefficient and subsequently discharge through Manning's equation in the compound meandering channels are found to be quite adequate.Hydrology 2018, 5, 47 2 of 21 hydraulic parameters. Formulation for Manning's roughness showing the gradient effect in channels having longitudinal slopes greater than 0.002 was derived by Jarrett [7]. Due to variation in flow depth, geometry and slope, the distinction of roughness coefficient in a straight channel as compared to a meandering channel were discussed by Arcement and Schneider [8]. Yen [9] proposed Manning's n for simple uniform flows taking account for a geometric measure, such as unevenness of the edge. Further, SCS method was linearized by James [10] for a meandering channel, named the Linearized SCS (LSCS) and recommended the Manning's roughness value for the different sinuosity channels. Shiono et al. [11] developed a model to predict discharge considering the Manning's n in the case of longitudinal slope and the meander effect of the channel. Jena [12] proposed an equation to calculate the n by considering the effect of channel width, longitudinal channel slope, and the flow depth of the channel. The variations of wetted area, wetted perimeter, and the velocity data on Manning's roughness coefficient by considering the flow simulation and sediment transport in irrigated channels was investigated by Mailapalli et al. [13]. Khatua et al. [14-16] formulated a mathematical equation for roughness coefficients by varying the sinuosity and geometry of the meandering compound channel. Xia et al. [17] and Barati [18-20] carried out the experiments for predicting discharge by taking care of the effect of bed roughness. Dash et al. [21] modeled the Manning's roughness coefficient by considering the aspect ratio, viscosity, slope of the bed, and sinuosity. Pradhan et al. [22] proposed an empirical formulation of predicting Manning's n by dimensional analysis for the compound meandering channel, which affects relative depth, width ratio, longitudinal channel slope, and sinuosity.In the recent years, ML techniques have been successfully implemented for predicting complex phenom...

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“…GMDH applies Procedure of LS approach of finding the optimal coefficients of quadratic polynomials. Gilbar and Pandya indicate that, a, improves the performance of self-organizing GMDH-type algorithms that is employed to build networks based on input-output observation data triples [10][11][12]. Optimizing nonlinear problem is part of the general problem of modeling in mathematical and computer applications.…”

Section: A Modeling Using Gmdh Neural Networkmentioning

confidence: 99%

Modeling and optimization of kanban controlled manufacturing system using improved-Group method of data handling

Pournasir

Alam

Marthandan

2011

2011 IEEE Student Conference on Research and Development

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one of the important sub-components in just-in-time (JIT) systems is number of optimal kanabn cards. Although a number of techniques exist for determining the number of kanbans, they have some shortcomings for operationalizing the kanban setting problem. In this paper, intelligent optimization method replace to old technique in kanban system to generate a structure that described the relationship between the operational factors and the number of kanbans with more accurately. We describe how the improved-Group method data handling algorithm can be used to improve the modeling and prediction accuracy when it is applied to kanban setting in just in time system.

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Modelling of multiple short-length-scale stall cells in an axial compressor using evolved GMDH neural networks

Cited by 86 publications

References 18 publications

Evaluation of GMDH networks for prediction of local scour depth at bridge abutments in coarse sediments with thinly armored beds

Evaluation of GMDH networks for prediction of local scour depth at bridge abutments in coarse sediments with thinly armored beds

Anticipate Manning’s Coefficient in Meandering Compound Channels

Modeling and optimization of kanban controlled manufacturing system using improved-Group method of data handling

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