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It is difficult to measure the change amount of the factorsIt is difficult to measure the change amount of the factors. In order to solve this problem, local economic cost sensitivity analysis is proposed. The method is a combination of neural network, sensitivity analysis, and economic cost disturbance. First, the nonlinear relationships between input and output variables with neural network for specific engineering are established. Second, same economic cost disturbance to each attribute of input variables is added. Third, the average amount of change of the output variables as the sensitivity coefficient of the input variables attributes is taken. Finally, by application in terms of Gas Emission, result validated that the method is effective. Introduction Sensitivity analysis is a method that can be quantitatively used to describe the degree of importance of model input variables to output variables [1]. In practical application, it can give priority to the attribute with large sensitivity coefficient. Currently, the sensitivity analysis has been widely applied to mineral engineering, such as mining rights assessment, slope stability, gas pressure and other fields [2-4]. In the practical engineering, in order to solve the problem that it is difficult to determine the nonlinear relationship between input variables and the output variables, and construct and develop a sensitivity analysis based on neural network [1]. Neural network is widely used in the mineral engineering. In the study of gas, the neural network is applied to the prediction model of gas emission, gas permeability and gas content [5-7]. However, sensitivity analysis based on neural network is seldom used in the mineral engineering. Sensitivity analysis is divided into local sensitivity analysis and global sensitivity analysis, and the most extensive application is local sensitivity analysis. At present, the widely used sensitivity analysis has the problem that it is difficult to measure the change amount of the factors. For example, in the literature [4], each input variable attribute was changed 10%. The difficulty of each input variables attribute change 10% is not the same, so the calculated sensitivity coefficient will reduce the guiding significance for engineering. Engineering activities are economic activities. In order to allow the disturbance of different input variables attribute comparable, this paper introduced the concept of economic cost and took output variable amount of change under each attribute in the same economic cost disturbance as the attribute's sensitivity coefficient. Local Economic Cost Sensitivity Analysis The local sensitivity analysis introduction economic cost is called local economic cost sensitivity analysis. Different from the traditional economic sensitivity analysis, object of local economic cost sensitivity analysis is not an economic system. In order to measure the difficulty of changing the input variables, introduced economic cost into sensitivity analysis. 3rd International Conference on Management, Education, Information and Control (MEICI 2015) © 2015. The authors Published by Atlantis Press 377 Assumed that an engineering model output variable is y and input variable is n x x x  , 2 1 .Non-linear relationship between the input variable and output variable is )  , ( 2 1 n x x x f y  .The non-linear relationship could be obtained by neural network training. For the attribute i , the relationship between the amount of change i a of i x and economic cost disturbance b could be expressed by the following equation: ) (b f a i i  (1) When 0  b , i a =0. When the economic cost disturbance value is small, in order to facilitate data processing, the relationship between the amount of change i a and economic cost disturbance b could be seen as linear relationship. First, in order to calculate the sensitivity coefficient, generated a set of small economic cost disturbances k b b b  , 2 1 , obeyed Gaussian distribution. Also, the researchers used the following equation to produce a k n matrix. ) ( j i ij b f a  (2) The k n matrix is shown below:

Local Economic Cost Sensitivity Analysis in the Application of Gas Emission