为提高无线传感器网络(WSNs)链路质量预测精度和降低噪声影响,提出了一种联合改进核FCM与智能优化SVR (improved kernel furry c-means and intelligent support vector regression,IKFCM-ISVR)的WSNs链路质量预测方案.首先将基于紧致度和离散度的有效性指数引入核FCM方法,实现样本集聚类个数自动划分;然后采用改进核FCM方法...
Short-term load forecasting for electric power systems using the PSO-SVR and FCM clustering techniques - Duan, Xie, et al. () Citation Context ... the state space in Equation (14). After building the generalized plant using the nominal model and appropriate weighting functions, the controllers...
FCM Based PSO-SVR Model Forecasted Forecasting Load (MW) Error (%) 2098.719 0.128 1975.301 −0.333 1811.568 −0.521 1755.734 −0.993 1699.189 −0.673 1705.629 0.414 1729.517 −2.495 1882.311 −2.798 2001.069 −2.653 2656.104 0.698 2617.052 0.998 2695.479 1.697 2680.677 1.097 2454.077 −...
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The result showed that FCM-SVR model estimates SSL (with RMSE, MAE, R-2, and NSE equal to 34,415.52 ton/day, 12,256.28 ton/day, 0.922, and 0.918 respectively in testing period) more accurate than other models. The RMSE value, which was 50% lower compared to other models, reveals ...
FCM聚类分析粒子群算法支持向量机回归算法针对窃电手段多样、隐蔽性强、窃电检测效率有待提高等问题,首先采用模糊C均值(FCM)聚类算法构造不同的用户负荷特征曲线,通过待测负荷曲线与相应特征曲线作对比初步确定疑似窃电用户;其次,采用粒子群算法优化的支持向量机回归模型对疑似窃电用户的用电行为进行检测.实验证明,所用...
针对窃电手段多样,隐蔽性强,窃电检测效率有待提高等问题,首先采用模糊C均值(FCM)聚类算法构造不同的用户负荷特征曲线,通过待测负荷曲线与相应特征曲线作对比初步确定疑似窃电用户;其次,采用粒子群算法优化的支持向量机回归模型对疑似窃电用户的用电行为进行检测.实验证明,所用方法缩小了窃电检测的范围,克服了窃电样本少...
Subsequently, calculated root mean square error (RMSE) between models and core data increased from 0.0376 to 0.03827 for Fuzzy SVR, while RMSE jumped from 0.0448 to 0.0517 for FCM SVR.doi:10.1007/s11600-022-00944-yMoosavi, NastaranBagheri, Majid...
针对窃电手段多样,隐蔽性强,窃电检测效率有待提高等问题,首先采用模糊C均值(FCM)聚类算法构造不同的用户负荷特征曲线,通过待测负荷曲线与相应特征曲线作对比初步确定疑似窃电用户;其次,采用粒子群算法优化的支持向量机回归模型对疑似窃电用户的用电行为进行检测.实验证明,所用方法缩小了窃电检测的范围,克服了窃电样本少...
An Improved SVR-FCM Method for Remaining Useful Life Prediction of Aircraft Enginesdoi:10.1109/CDS49703.2020.00020Data sciencePredicting the remaining useful life (RUL) remains an important part in prognostics and health management (PHM) discipline. But the complexity of machine system and the noise ...