Issue Date: 6-7 March 2010rnrntOn page(s): rnt345rnttrn- 347rnrnrnLocation: Wuhan, ChinarnrnPrint ISBN: 978-1-4244-6388-6rnrnrnrnttrnDigital Object Identifier: href='http://dx.doi.org/10.1109/ETCS.2010.477' target='_blank'>10.1109/ETCS.2010.477 rnrnDate of Current Version: trnrnt2010-05-06 14:33:52.0rnrnt rntt class="body-text">rntname="Abstract">>Abstractrn>Inspired by the so-called "divide-and-conquer" principle that is often used to attack a complex problem by dividing it into simpler problems, a three-stage SVM ensemble algorithm is proposed to improve its prediction accuracy and generalization performance for chaotic time series. In the first stage, Fuzzy C-means clustering algorithm is adopted to partition the input datas;
FCM clustering algorithm; PSO; SVM ensemble; chaotic time series prediction; composite kernels;
机译:基于LS-SVM和混沌突变进化规划的非线性时间序列预测参数优化
机译:集成神经网络在混沌时间序列预测中的并行性能
机译:集成的自生成神经网络在混沌时间序列预测问题上的并行性能
机译:混沌时间序列预测的三阶段支持向量机集成算法
机译:利用集成神经网络和Taguchi的实验设计的协同作用,通过残差分析对混沌时间序列进行预测。
机译:基于改进遗传模拟退火算法的函数表达方法预测非线性混沌时间序列
机译:改进的量化核最小均方算法用于混沌时间序列预测
机译:混沌时间序列的时延嵌入预测误差统计