A Distillation Column Fault Diagnosis Method Based on Improved Particle Swarm Optimization Support Vector Machine
A technology of support vector machine and improved particle swarm, which is applied in computer components, data processing applications, prediction, etc., to improve the effect of easily falling into local optimum and improve classification accuracy
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[0036] Such as figure 1 As shown, a rectification column fault diagnosis method for improved particle swarm optimization support vector machine, the method specifically includes the following steps:
[0037] Step 1: Setting of initial value of particle swarm, given input data X={X 1 ,...,X n} and learning objective y={y 1 ,...,y n} are all derived from the failure data of the rectification tower, where T max The maximum number of iterations is 300, set w as the inertia weight of 0.9, and the acceleration factor c 1 is 1.6, the acceleration factor c 2 1.5, V max The initial maximum set speed is 120, X max Set the position to 180 for the initial maximum. Given the parameter C, the range of σ is [0,100], C is the penalty coefficient, and σ is the selected RBF function (K(x i ,x j ) = exp(||x i -x j || 2 / σ 2 )) As a kernel, a parameter of this function, i=1,2,...n,j=1,2,...n,x i =[x 1 ,...,x n ]∈X represents the multiple feature space contained in each sample of ...
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