Support vector machine （SVM） is powerful to solve some problems such as nonlinear classification, function estimation and density estimation. To consider the chaotic fh （frequency hopping）-code＇s characters in ch...Support vector machine （SVM） is powerful to solve some problems such as nonlinear classification, function estimation and density estimation. To consider the chaotic fh （frequency hopping）-code＇s characters in chaotic dynamic system, the forecasting model of the support vector machine in combination with Takens＇ delay coordinate phase reconstruction of chaotic times is established and the least squares model for large-scale problems is used in local training for this model. Finally, a fh-code series generated by Logistic-Kent mapping is applied to verify the local prediction model. Simulation results show that the high accuracy and fault tolerant SVM model has an excellent performance in predicting the fh code, with a very low mean square error and a high relative coefficient.展开更多
基金the National Natural Science Foundation of China(10577007)Special Fund of Anti-InterferenceTechnology in Tactical Communication Defend Lab(51434020105ZS04).
文摘Support vector machine （SVM） is powerful to solve some problems such as nonlinear classification, function estimation and density estimation. To consider the chaotic fh （frequency hopping）-code＇s characters in chaotic dynamic system, the forecasting model of the support vector machine in combination with Takens＇ delay coordinate phase reconstruction of chaotic times is established and the least squares model for large-scale problems is used in local training for this model. Finally, a fh-code series generated by Logistic-Kent mapping is applied to verify the local prediction model. Simulation results show that the high accuracy and fault tolerant SVM model has an excellent performance in predicting the fh code, with a very low mean square error and a high relative coefficient.
