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Application of support vector machine optimized by improved ant colony optimization algorithm in power coal blending prediction

Wei SUN, Xin Gao


For current situation of power coal blending prediction, this paper proposed a new prediction model based on support vector machine(SVM) optimized by ant colony optimization algorithm(ACO). Firstly, use improved ant colony algorithm to optimize the parameter C of SVM and parameter sigma of kernel function; secondly, use the obtained parameters to build ACO-SVM prediction model; finally, applied the proposed model to the actual example. The verification results show that the proposed ACO-SVM prediction model can achieve higher prediction accuracy than basic SVM prediction model and Weighting average method under the condition of small samples; and it has a high practical value and broad application in coal blend field.


Power blending coal, Ant colony optimization, Support Vector Machine, Prediction,Volatile factor.

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