Pump performance analysis based on least squares support vector machine

Performance curve of pump is important basis of pump types selecting, optimal operation and pump station running. The curve is usually obtained from experiment or by fitting the experimental data of performance graph, but those methods are complex, high expense and imprecision. Based on optimum parameter selection with cross validation, the least squares support vector machine (LSSVM) method was presented for pump performance forecast in the light of the difficulty of the above two methods. Complicated and strong nonlinear pump performance curve was simulated by network design and conformation of LSSVM learning algorithm and the optimized SVM parameters were selected by the method of network searching and cross validation. Compared the errors with output values of the optimized model, test value and output value from polynomial fitting and RBFNN, LSSVM whose parameter was optimized with cross validation had excellent ability of nonlinear modeling and generalization. It gained high precision under limited learning samples (mean relative error is 0.02378%) and provided a simple and feasible intelligent approach for pump performance analysis.