Multiparameter Sensitivity Analysis of Operational Energy Efficiency for Inland River Ships Based on Backpropagation Neural Network Method

2015 ◽  
Vol 49 (1) ◽  
pp. 148-153 ◽  
Author(s):  
Xinping Yan ◽  
Xing Sun ◽  
Qizhi Yin
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Energy Efficiency ◽  
Variable Parameter ◽  
Energy Efficiency Improvement ◽  
Inland River ◽  
Network Method ◽  
Sensitivity Analysis Method ◽  
Operational Energy ◽  
Set Up

AbstractWith the introduction of energy efficiency operational indicator (EEOI) to inland river ships, a multiparameter sensitivity analysis method was proposed to analyze the parameters affecting the operational energy efficiency of inland river ships. On the basis of experimental data, a model based on a backpropagation artificial neural network (BP-ANN) for predicting the EEOI was set up. The accuracy of this predictive model was verified. On the basis of weights and threshold values of each variable parameter gained in the trained BP-ANN, a Garson algorithm was used for calculating the parameter sensitivity factors. Results showed that, besides the engine speed, the environment conditions would also play a big part in the operational energy efficiency of inland river ships. The conclusion provides a foundation for engaging the energy efficiency improvement strategies for inland river ships.

Optimization of the Hot Pressing Parameters of Nanocomposite Ceramic Tool and Die Materials: I. with BP Neural Network Method

2010 ◽  
Vol 154-155 ◽  
pp. 1114-1118
Author(s):  
Jing Jie Zhang ◽  
Chong Hai Xu ◽  
Ming Dong Yi ◽  
Hui Fa Zhang ◽  
Xing Hai Wang
Keyword(s):  
Neural Network ◽  
Hot Pressing ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Ceramic Tool ◽  
Network Method ◽  
Die Materials ◽  
Die Material ◽  
Set Up ◽  
The Relationship

In this paper, back propagation neural network was used in the optimum design of the hot pressing parameters of an advanced ZrO2/TiB2/Al2O3 nanocomposite ceramic tool and die material. The BP algorithm could set up the relationship well between the hot pressing parameters and mechanical property of nanocomposite ceramic tool and die materials. After analyzed the predicted results, the best predicted results were the sintering temperature was 1420°C and the holding time was 60min. Under these hot pressing parameters, the best flexural strength and the best fracture toughness of the material could be obtained.

scholarly journals ENERGY EFFICIENCY SAVING FINANCING IN INDONESIA: LCC ANALYSIS

2020 ◽  
Vol 5 (2) ◽  
pp. 202-207
Author(s):  
Eka Sudarmaji ◽  
Yuli Ardianto
Keyword(s):  
Game Theory ◽  
Sensitivity Analysis ◽  
Energy Efficiency ◽  
Life Cycle ◽  
Life Cycle Cost ◽  
Initial Model ◽  
Analytic Hierarchy ◽  
Investment Returns ◽  
Set Up ◽  
Hierarchy Process

This paper to set up an initial model in developing the model for Energy Saving Companies in Indonesia in assessing alternative financing for Energy Efficiency Saving in Indonesia. The reviewed for all the energy efficiency saving advantages cover the upfront investment costs are presented. The model is using the Analytic Hierarchy Process (AHP) and life cycle cost (LCC) analysis, with sensitivity analysis, is presented under possible a game-theory process. On some occasions, these alternative financing values are comparing to other similar investment returns as well as the risks

scholarly journals Ten-year estimation of Oriental beech (Fagus orientalis Lipsky) volume increment in natural forests: a comparison of an artificial neural networks model, multiple linear regression and actual increment

2021 ◽  
Author(s):  
Mahmoud Bayat ◽  
Pete Bettinger ◽  
Majid Hassani ◽  
Sahar Heidari
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Artificial Neural Network ◽  
Productive Capacity ◽  
Natural Forests ◽  
Neural Network Method ◽  
Volume Increment ◽  
Network Method ◽  
Artificial Neural Network Method ◽  
Artificial Neural

Abstract Determining forest volume increment, the potential of wood production in natural forests, is a complex issue but is of fundamental importance to sustainable forest management. Determining potential volume increment through growth and yield models is necessary for proper management and future prediction of forest characteristics (diameter, height, volume, etc.). Various methods have been used to determine the productive capacity and amount of acceptable harvest in a forest, and each has advantages and disadvantages. One of these methods involves the artificial neural network techniques, which can be effective in natural resource management due to its flexibility and potentially high accuracy in prediction. This research was conducted in the Ramsar forests of the Mazandaran Province of Iran. Volume increment was estimated using both an artificial neural network and regression methods, and these were directly compared with the actual increment of 20 one-hectare permanent sample plots. A sensitivity analysis for inputs was employed to determine which had the most effect in predicting increment. The actual average annual volume increment of beech was 4.52 m3ha−1 yr−1, the increment was predicted to be 4.35 and 4.02 m3ha−1 yr−1 through the best models developed using an artificial neural network and using regression, respectively. The results showed that an estimate of increment can be predicted relatively well using the artificial neural network method, and that the artificial neural network method is able to estimate the increment with higher accuracy than traditional regression models. The sensitivity analysis showed that the standing volume at the beginning of the measurement period and the diameter of trees had the greatest impact on the variation of volume increment.

Sensitivity Analysis in Artificial Neural Network and it's Applications on the Research of Attributes Correlation

2013 ◽  
Vol 785-786 ◽  
pp. 1441-1446
Author(s):  
Hong Yan Lin ◽  
Hai Hua Xing
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Artificial Neural Network ◽  
Calculation Method ◽  
Strong Correlation ◽  
Analysis Method ◽  
Model Input ◽  
Network Connection ◽  
Sensitivity Analysis Method ◽  
Artificial Neural

Sensitivity analysis method can evaluate the importance of model input attributes. A multivariable sensitivity analysis method based on neural network connection weights and a calculation method of attributes correlation are proposed in this paper, and are applied to the research of attributes correlation. To verify the effectiveness of the proposed methods, this study employed a man-made example and a UCI-IRIS dataset to test the performance of the method. The results show that the sensitivity analysis method can really identify important and strong correlation attributes of model, and can simplify the model effectively, and can improve the accuracy of the model.

Sensitivity analysis of CHF parameters under flow instability by using a neural network method

2014 ◽  
Vol 71 ◽  
pp. 211-216 ◽  
Author(s):  
Jingjing Li ◽  
Tao Zhou ◽  
Zhongyun Ju ◽  
Qijun Huo ◽  
Zejun Xiao
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Flow Instability ◽  
Neural Network Method ◽  
Network Method

scholarly journals Prediction-Based Maintenance of Existing Bridges Using Neural Network and Sensitivity Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Pengyong Miao
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Predictive Ability ◽  
Relative Effect ◽  
Analysis Method ◽  
Ann Model ◽  
Sensitivity Analysis Method ◽  
Artificial Neural Network Ann ◽  
Existing Bridges ◽  
Inspection Data

Bridge deterioration is affected by various factors. However, neither the relationships between these factors and deterioration are explicitly determined, nor the relative effect of each factor on deterioration is well understood. This study proposed a methodology to resolve these issues by integrating an artificial neural network (ANN) and sensitivity analysis method. The ANN was used to predict deterioration, and the sensitivity analysis method was applied to evaluate the influence of each factor on deterioration. Testing the methodology with 3,368 bridge inspection data pieces indicates that (1) the developed ANN obtained an accuracy of about 65%; and (2) seven factors were identified affecting deterioration. The established ANN model has equivalent performance for three deterioration grades and four types of bridges. Two sensitivity analysis (the Shapley value and the Sobol indices) methods were compared, and they identified the same five most important factors. Consequently, the methodology can effectively avoid the uncertainty of factors on deterioration by providing a relative importance list of factors. The methodology’s predictive ability and factor importance identification ability make it suitable for decision-makers to understand the deterioration situations and to schedule a further inspection and corresponding maintenance strategies.

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