An Integrated Artificial Neural Network Fuzzy C-Means-Normalization Algorithm for performance assessment of decision-making units: The cases of auto industry and power plant

Azadeh, A.; Saberi, Morteza; Anvari, M.

doi:10.1016/j.cie.2010.11.016

Cited by 22 publications

(17 citation statements)

References 42 publications

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“…Normalization is a compression operation formed between the lower and upper limits of the values that are entered to the data set. Therefore, different methods can be used in normalization operations [27,28]. In the present study, we preferred to use min-max normalization method.…”

Section: Artificial Neural Network Applicationmentioning

confidence: 98%

Artificial neural network prediction of aging effects on the wear behavior of IN706 superalloy

2015

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Section: Artificial Neural Network Applicationmentioning

confidence: 98%

Artificial neural network prediction of aging effects on the wear behavior of IN706 superalloy

2015

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“…In all these methodologies, the frontier is defined by the most efficient DMU of the sample. Mathematically, frontier methods were introduced as highreliability analysis tools and have been largely used for studies in the manufacturing field ([6], [7], [8], [9], [10]). …”

Section: Literature Reviewmentioning

confidence: 99%

Trust-based performance measurement using fuzzy operators

Azadeh

Zia

Saberi

et al. 2014

2014 9th IEEE Conference on Industrial Electronics and Applications

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Performance assessment is a critical aspect for any organization, as it provides them with the means to measure their performance. Decision makers and top management need to gain a comprehensive view of the capabilities and performance of decision making units (DMU's) in order to make efficient decisions and beneficial improvements. In this study a novel model has been proposed to place performance assessment outputs in linguistic form, which utilize proper trust labels. Trust labels provide explicit qualitative scales, instead of representing pure numerical data, which are more meaningful for top manager. Fifteen scenarios are formed based on two main factors: the number of decision making units and the number of timeslots, which together form the basis of the proposed method for performance assessment. The efficiency rates of the current, previous and following years, along with the average efficiency and standard deviation, are the five inputs to this model. The approach uses time series forecasting to predict the future efficiency rate and is armed with an Auto Correlation Function (ACF) for input selection. The model utilizes fuzzy t-norm and snorm as the final modeling tools. To show the applicability and superiority of the proposed model, it is applied to a data set provided by running a simulation structured by a unique logic.

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“…ANNs ensure the prediction of the physical system variables without requiring mathematical expressions. ANNs techniques have been used by some researchers to predict the engine performance and exhaust emissions by means of the fuel properties such as cetane number, density, volatility, oxygen and sulfur content [4,5]. ANNs have also been used in analyzing and predicting the performance and exhaust emissions of diesel engines [6].…”

Section: Introductionmentioning

confidence: 99%

Prediction of performance and exhaust emission of a marine electronic control diesel engine based on modeling of BP neural networks

Wang

Zhang

et al. 2011

Proceedings of 2011 International Conference on Electronic &Amp; Mechanical Engineering and Information Technology

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The aim of this study is to predict the marine diesel engine performance and exhaust emission values building a new approach based on ANNs. In this study, two stages were carried out: First, the experimental data was obtained by testing performance and exhaust emission of marine diesel on the bed. Second, ANN modeling was successfully trained by experimental data, and then the model was used to predict engine emission values. The result showed that the values produced by ANNs were parallel to the experimental results.

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An Integrated Artificial Neural Network Fuzzy C-Means-Normalization Algorithm for performance assessment of decision-making units: The cases of auto industry and power plant

Cited by 22 publications

References 42 publications

Artificial neural network prediction of aging effects on the wear behavior of IN706 superalloy

Artificial neural network prediction of aging effects on the wear behavior of IN706 superalloy

Trust-based performance measurement using fuzzy operators

Prediction of performance and exhaust emission of a marine electronic control diesel engine based on modeling of BP neural networks

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