A Radial Basis Function Neural Network for Stochastic Frontier Analyses of General Multivariate Production and Cost Functions

Pendharkar, Parag C.

doi:10.1007/s11063-022-11137-5

Cited by 3 publications

(2 citation statements)

References 35 publications

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“…For example, Lu et al (2009) proposed a method that uses a feedforward NN to estimate the conditional mean and variance of the input and output data of the DMUs, and then uses a stochastic DEA model to calculate the efficiency scores (Boubaker et al, 2023) (Wang et al, 2022) (Kainthura & Sharma, 2022). Zhang et al (2014) proposed a similar method that uses a radial basis function NN to estimate the conditional distribution of the input and output data of the DMUs (Pendharkar, 2023).…”

Section: Introductionmentioning

confidence: 99%

Integrating the neural network into the stochastic DEA model

Sihotang,

Lavemaau,

Riandari

et al. 2023

Idea

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The novelty of integrating neural networks (NNs) into the stochastic DEA model lies in the ability to address some of the limitations of traditional DEA models and improve the accuracy and efficiency of efficiency measurement and prediction. By integrating NNs, the stochastic DEA model can capture the complex and non-linear relationships between the input and output variables of the decision-making units (DMUs) and handle uncertainty in the input and output data. This is achieved by using the NN to estimate the distribution of the input and output data and then using the stochastic DEA model to calculate the efficiency scores based on these estimated distributions. Furthermore, the integration of NNs into the stochastic DEA model allows for the development of hybrid models that combine the strengths of both techniques. For example, some researchers have proposed using genetic algorithms or other optimization techniques to optimize the input and output weights of the stochastic DEA model, which are then used to calculate the efficiency scores based on the estimated distributions from the NN. This results in a more accurate and efficient efficiency measurement and prediction model. Another novelty of integrating NNs into the stochastic DEA model is the potential for enhancing the interpretability of the model. While NNs are often considered as black-box models, several methods have been proposed to enhance the interpretability of NN-based stochastic DEA models. These methods include using feature importance analysis or visualization techniques to identify the most important input and output variables that contribute to the efficiency scores. Overall, the integration of NNs into the stochastic DEA model represents a novel approach to addressing the limitations of traditional DEA models and improving the accuracy and efficiency of efficiency measurement and prediction under uncertainty. The development of hybrid models and methods to enhance interpretability further add to the novelty and potential impact of this research.

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Section: Introductionmentioning

confidence: 99%

Integrating the neural network into the stochastic DEA model

Sihotang,

Lavemaau,

Riandari

et al. 2023

Idea

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“…This assumption makes it unsuitable for tasks involving viscous drag prediction. RBF is a local approximation method, and it does not provide output values if the input variables are far away from the range of the database [28,29]. On the other hand, ANN is a global approximation method that is widely used for approximating highly nonlinear relationships [30][31][32].…”

mentioning

confidence: 99%

Dynamic Analysis of a Moored Spar Platform in a Uniform Current: Fluid Load Prediction Using a Surrogate Model

Wei,

Zhu,

Cao

et al. 2024

JMSE

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A moored spar platform, equipped with various instruments, serves as a crucial tool in hydrological monitoring. However, conducting dynamic analyses of a single spar that endures wind and current requires significant amount of computational time. To address this challenge, this study proposes an efficient surrogate model to represent fluid loads. A database is established to capture the relationship between fluid loads, spar displacements and uniform currents based on a numerical model of the spar. Subsequently, an artificial neural network method is employed to construct the surrogate model. Finally, the surrogate model is integrated with a numerical model of the cable, developed using the lumped mass method, to create a coupled model of the moored spar. The dynamic responses of this coupled model align closely with those obtained from the purely numerical model, demonstrating the efficacy of the surrogate model in capturing fluid loads on the spar. In addition to the surrogate model generation approach, this research provides an efficient method to couple the surrogate model with the numerical model in dynamic analysis of floating systems in uniform currents.

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Does Improving Technical Efficiency Increase Food Production? An Analysis of Wheat Farms in Afghanistan

SAMIM,

DENG,

AMINI

et al. 2024

Singapore Econ. Rev.

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Afghanistan faces serious challenges in terms of food security, which highlights the importance of agricultural productivity growth. This study assessed the impact of technical efficiency (TE) of wheat production on farmers’ household food security by improving food production using ex-post non-experimental data from Takhar Province, Afghanistan. Our empirical approach is based on the Cobb–Douglas stochastic frontier model and the Household Food Insecurity Access Scale (HFIAS). We found that farmers’ TE significantly improved farmers’ households’ food security by improving food production. Furthermore, the average TE score of food-secure farmers was 72.0%, whereas that of food-insecure farmers was 66.2%, and the difference was statistically significant. In conclusion, our findings underscore the essential role of improving farmers’ TE in enhancing food security in farming households in Afghanistan.

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A Radial Basis Function Neural Network for Stochastic Frontier Analyses of General Multivariate Production and Cost Functions

Cited by 3 publications

References 35 publications

Integrating the neural network into the stochastic DEA model

Integrating the neural network into the stochastic DEA model

Dynamic Analysis of a Moored Spar Platform in a Uniform Current: Fluid Load Prediction Using a Surrogate Model

Does Improving Technical Efficiency Increase Food Production? An Analysis of Wheat Farms in Afghanistan

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