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Pressure Model of Control Valve Based on LS-SVM with the Fruit Fly Algorithm

机译：基于果蝇算法的基于LS-SVM的控制阀压力模型

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Control valve is a kind of essential terminal control component which is hard to model by traditional methodologies because of its complexity and nonlinearity. This paper proposes a new modeling method for the upstream pressure of control valve using the least squares support vector machine (LS-SVM), which has been successfully used to identify nonlinear system. In order to improve the modeling performance, the fruit fly optimization algorithm (FOA) is used to optimize two critical parameters of LS-SVM. As an example, a set of actual production data from a controlling system of chlorine in a salt chemistry industry is applied. The validity of LS-SVM modeling method using FOA is verified by comparing the predicted results with the actual data with a value of MSE 2.474 × 10−3. Moreover, it is demonstrated that the initial position of FOA does not affect its optimal ability. By comparison, simulation experiments based on PSO algorithm and the grid search method are also carried out. The results show that LS-SVM based on FOA has equal performance in prediction accuracy. However, from the respect of calculation time, FOA has a significant advantage and is more suitable for the online prediction.
机译：控制阀是一种必不可少的终端控制组件，由于其复杂性和非线性性，很难用传统方法进行建模。本文提出了一种使用最小二乘支持向量机（LS-SVM）对控制阀上游压力进行建模的新方法，该方法已成功地用于非线性系统的辨识。为了提高建模性能，采用果蝇优化算法（FOA）对LS-SVM的两个关键参数进行优化。例如，应用了盐化学工业中氯控制系统的一组实际生产数据。通过将预测结果与实际数据（MSE值为2.474×10 −3 ）进行比较，验证了使用FOA的LS-SVM建模方法的有效性。而且，证明了FOA的初始位置不影响其最佳能力。通过比较，还进行了基于PSO算法和网格搜索方法的仿真实验。结果表明，基于FOA的LS-SVM在预测精度上具有同等的性能。但是，从计算时间的角度来看，FOA具有明显的优势，并且更适合在线预测。

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