Application of radial basis function and generalized regression neural networks in non-linear utility function specification for travel mode choice modelling

Çelikoğlu, Hilmi Berk

doi:10.1016/j.mcm.2006.02.002

Cited by 121 publications

(54 citation statements)

References 23 publications

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“…Tingsanchali and Gautam (2000) applied ANN and stochastic hydrologic models to forecast the flood in two river basins in Thailand. GRNN method have also been used for many specific studies (Cigizoglu, 2005;Cigizoglu and Alp, 2006;Kim et al, 2004;Ramadhas et al, 2006;Celikoglu and Cigizoglu, 2007;Celikoglu, 2006). On the other hand, Fuzzy Logic (FL) method…”

Section: Introductionmentioning

confidence: 99%

Comparison of Artificial Intelligence Techniques for river flow forecasting

Fırat

2008

Hydrol. Earth Syst. Sci.

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Abstract. The use of Artificial Intelligence methods is becoming increasingly common in the modeling and forecasting of hydrological and water resource processes. In this study, applicability of Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) methods, Generalized Regression Neural Networks (GRNN) and Feed Forward Neural Networks (FFNN), and Auto-Regressive (AR) models for forecasting of daily river flow is investigated and Seyhan River and Cine River was chosen as case study area. For the Seyhan River, the forecasting models are established using combinations of antecedent daily river flow records. On the other hand, for the Cine River, daily river flow and rainfall records are used in input layer. For both stations, the data sets are divided into three subsets, training, testing and verification data set. The river flow forecasting models having various input structures are trained and tested to investigate the applicability of ANFIS and ANN and AR methods. The results of all models for both training and testing are evaluated and the best fit input structures and methods for both stations are determined according to criteria of performance evaluation. Moreover the best fit forecasting models are also verified by verification set which was not used in training and testing processes and compared according to criteria. The results demonstrate that ANFIS model is superior to the GRNN and FFNN forecasting models, and ANFIS can be successfully applied and provide high accuracy and reliability for daily river flow forecasting.

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Section: Introductionmentioning

confidence: 99%

Comparison of Artificial Intelligence Techniques for river flow forecasting

Fırat

2008

Hydrol. Earth Syst. Sci.

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“…GRNN displays the difference of radial basis neural networks, which are developed from kernel regression networks [29][30][31]. In contrast to a backpropagation network, an iterative training procedure is unnecessary for GRNN.…”

Section: Development Of the Grnn Modelmentioning

confidence: 99%

Prediction of Frost‐Heaving Behavior of Saline Soil in Western Jilin Province, China, by Neural Network Methods

Zhang

Wang

Huo

et al. 2017

Mathematical Problems in Engineering

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In this study, backpropagation neural network (BPNN) and generalized regression neural network (GRNN) approaches are used to predict the frost-heaving ratio (FR) of the saline soil specimen collected from Nong'an, Western Jilin, China. Four variables, namely, water content (WC), compactness, temperature, and content of soluble salts (CSS), are considered in predicting FR. A total of 360 pieces of data, collected from the experimental results, in which 30 pieces of data were selected randomly as the testing set data and the rest of the data were treated as the training set data, are applied to develop the prediction models. The predicted data from the models are compared with the experimental data. Then, the results of the two approaches are compared to obtain a relatively reliable model. Results indicate that the prediction model for the FR of saline soil in Nong'an can be successfully established using the GRNN method. Four new GRNN models are constructed for sensitivity analysis to assess the influence degree of the influencing factors, and the results indicate that water content is the most influential variable in the FR of the saline soil specimen, whereas content of soluble salts is the least influential variable.

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“…It can be treated as a normalized radial basis neural networks in which there is a hidden neuron centered at every training case. These radial basis function units are generally probability density function such as the Gaussian (Celikoglu 2006). The use of a probability density function is particularly gainful due to its ability to converge to the underlying function of the data with only limited training data available.…”

Section: Generalized Regression Neural Network (Grnn)mentioning

confidence: 99%

Generalized regression and feed-forward back propagation neural networks in modelling porosity from geophysical well logs

Konaté

Pan

Khan

et al. 2014

J Petrol Explor Prod Technol

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Geophysical formation evaluation plays a fundamental role in hydrocarbon exploration and production processes. It is a process which describes different reservoir parameters using well field data. Porosity is one of the parameters that determines the amount of oil present in a rock formation and research in this area is mainly carried out by engineers and geoscientists in the petroleum industry. Accurate prediction of porosity is a difficult problem. This is mostly due to the failure in the understanding of spatial porosity parameter distribution. Artificial neural networks have proved to be a powerful tool for mapping complicated and non-linear relationships in petroleum studies. In this study, we analyze and compare generalized regression neural network (GRNN) and feedforward back propagation neural network (FFBP) in modeling porosity in Zhenjing oilfield data. This study is calibrated on four wells of Zhenjing oilfield data. One well was used to find an empirical relationship between the well logs and porosity, while the other three wells were used to test the model's predictive ability in the field, respectively. The findings proved that the GRN network can make more accurate and credible porosity parameter estimation than the commonly used FFBP network. Artificial intelligence can be exploited as a powerful instrument for predicting reservoir properties in geophysical formation evaluation and reservoir engineering in petroleum industry.

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Application of radial basis function and generalized regression neural networks in non-linear utility function specification for travel mode choice modelling

Cited by 121 publications

References 23 publications

Comparison of Artificial Intelligence Techniques for river flow forecasting

Comparison of Artificial Intelligence Techniques for river flow forecasting

Prediction of Frost‐Heaving Behavior of Saline Soil in Western Jilin Province, China, by Neural Network Methods

Generalized regression and feed-forward back propagation neural networks in modelling porosity from geophysical well logs

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