基于NWAPSO参数全局寻优的LS—SVM电站风机性能预测

风机的性能曲线是风机选型和优化运行的重要依据．通常该曲线通过试验试验数据和性能图表上的数据进行曲线拟合获得．由于该曲线非线性很强，传统方法复杂昂贵，而且拟合精度不高。针对以上不足，提出了一种基于非线性权重自适应粒子群优化（NWAPSO）参数全局寻优的最小二乘支持向量机（LS—SVM）风机性能预测方法。通过最小二乘支持向量机建模，并应用非线性权重自适应粒子群优化算法对模型参数进行全局寻优，得到具有较高精度的风机性能曲线。计算结果表明，根据文中方法建立的模型很简洁，只需要知道少量的训练样本就能建立，可以比较精确的预测风机性能，具有较显著的工程应用价值。

Performance Forecast in the Fan of Power Station with LS-SVM Based on Parameter Optimization by NWAPSO

The performance curves of the fans are essential basis of type selection and operation optimization of fans.Generally,these curves are obtained by curve fitting with data from experiments or performance diagrams.Nevertheless,the curves are highly nonlinear,therefore,the traditional methods are expensive and the fitting accuracy is sketchy.In allusion to the sketchiness above,a new algorithm with the least squares support vector machine(LS-SVM) based on parameter optimization by adaptive particle swarm optim...