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.

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.

scholarly journals Development of Effective Artificial Neural Network Model using Sequential Sensitivity Analysis and Randomized Training

2021 ◽  
Vol 10 (6) ◽  
pp. 12-20
Author(s):  
Akshay Daydar ◽  
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Artificial Neural Network ◽  
Research Work ◽  
Large Data ◽  
Qualitative Reasoning ◽  
Machine Learning Algorithms ◽  
Finance Industry ◽  
Training Approach ◽  
Artificial Neural

As the machine learning algorithms evolve, there is a growing need of how to train the algorithm effectively for the large data with available resources in practically less time. The paper presents an idea of developing an effective model that focuses on the implementation of sequential sensitivity analysis and randomized training approach which can be one solution to this growing need. Many researchers focused on the implementation of sensitivity analysis to eliminate the insignificant features ands reduce the complexity in data selection. These sensitivity analysis methods relatively take a large time for validation through modeling and hence found impractical for large data. On the other hand, the randomized training approach was found to be the most popular approach for training the data but there is a very brief explanation available in research articles on how this training method is meaningful in getting higher accuracy. The current work focuses on the use of sequential sensitivity analysis and randomized training in an artificial neural network (ANN) for high dimensionality thermal power plant data. The sequential sensitivity analysis (SSA) technique includes the use of correlation analysis (CA), Analysis of variance (ANOVA), Akaike information criterion (AIC) in a sequential manner to reduce the validation time for all possible feature combinations. Only selected combinations are then tested against different training methods such as downward extrapolation, upward extrapolation, interpolation and randomized training in ANN. The paper also focuses on suggesting the significance of training with randomized training with comparison-based qualitative reasoning. The statistical parameters, mean square error (RMSE), Mean absolute relative difference (MARD) and R Square (R^2)were accessed for validation purposes. The research work mainly useful in the field of Ecommerce, Finance, industry and in facilities where large data is generated.

Research on Speed Limit of Freeway Tunnel Group Based on Artificial Neural Network

2011 ◽  
Vol 97-98 ◽  
pp. 892-895
Author(s):  
Sheng Rui Zhang ◽  
Hui Li Yan ◽  
Wen Jing Niu ◽  
Rui Cao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Time Factors ◽  
Speed Limit ◽  
Operating Speed ◽  
Output Variable ◽  
Dynamic Factors ◽  
Sensitivity Analysis Method ◽  
Artificial Neural ◽  
Input Variables

According to the traffic conditions in the typical freeway tunnel group in China, an artificial neural network model is constructed for the purpose of predicting the operating speed in freeway tunnel group in this paper. In this model, some input variables are selected from four aspects, including time factors, traffic dynamic factors, road conditions and tunnel environment, and the output variable is the operating speed. Then the sensitivity analysis method is selected to study the effects of input variables on output variable. The results show that this algorithm can avoid the difficulty of constructing traffic flow model comparing to the traditional algorithm, and it is suitable to realize online modeling for speed limit of freeway tunnel group. Results of this research are practical and effective, and it may provide a theoretical foundation for speed limit of freeway tunnel group.

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