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...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方法对链路质量样本数据进行处理,获得样本聚类隶属度;在此基础上,构建群居蜘蛛优化SVR预测模型,采用基于"动态折射"学习机制的群集蜘蛛对模型参数进行优化,得到不同聚类最佳SVR参数组合;最后采用IKFCM-ISVR算法对不同实验场景下的WSNs链路数据进行预测评估.仿真结果表明,同其它预测算法相比,该算法预测精度提高了36.8~68.4%.展开更多
In order to accurately describe the dynamic characteristics of flight vehicles through aerodynamic modeling, an adaptive wavelet neural network (AWNN) aerodynamic modeling method is proposed, based on subset kernel pr...In order to accurately describe the dynamic characteristics of flight vehicles through aerodynamic modeling, an adaptive wavelet neural network (AWNN) aerodynamic modeling method is proposed, based on subset kernel principal components analysis (SKPCA) feature extraction. Firstly, by fuzzy C-means clustering, some samples are selected from the training sample set to constitute a sample subset. Then, the obtained samples subset is used to execute SKPCA for extracting basic features of the training samples. Finally, using the extracted basic features, the AWNN aerodynamic model is established. The experimental results show that, in 50 times repetitive modeling, the modeling ability of the method proposed is better than that of other six methods. It only needs about half the modeling time of KPCA-AWNN under a close prediction accuracy, and can easily determine the model parameters. This enables it to be effective and feasible to construct the aerodynamic modeling for flight vehicles.展开更多
文摘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方法对链路质量样本数据进行处理,获得样本聚类隶属度;在此基础上,构建群居蜘蛛优化SVR预测模型,采用基于"动态折射"学习机制的群集蜘蛛对模型参数进行优化,得到不同聚类最佳SVR参数组合;最后采用IKFCM-ISVR算法对不同实验场景下的WSNs链路数据进行预测评估.仿真结果表明,同其它预测算法相比,该算法预测精度提高了36.8~68.4%.
基金Project(51209167) supported by Youth Project of the National Natural Science Foundation of ChinaProject(2012JM8026) supported by Shaanxi Provincial Natural Science Foundation, China
文摘In order to accurately describe the dynamic characteristics of flight vehicles through aerodynamic modeling, an adaptive wavelet neural network (AWNN) aerodynamic modeling method is proposed, based on subset kernel principal components analysis (SKPCA) feature extraction. Firstly, by fuzzy C-means clustering, some samples are selected from the training sample set to constitute a sample subset. Then, the obtained samples subset is used to execute SKPCA for extracting basic features of the training samples. Finally, using the extracted basic features, the AWNN aerodynamic model is established. The experimental results show that, in 50 times repetitive modeling, the modeling ability of the method proposed is better than that of other six methods. It only needs about half the modeling time of KPCA-AWNN under a close prediction accuracy, and can easily determine the model parameters. This enables it to be effective and feasible to construct the aerodynamic modeling for flight vehicles.
