Chaos Real-Time Recognition of Traffic Flow by Using SVM Rough Neural Network

The real-time recognition problem of chaos in traffic flow was studied by using support vector machine(SVM) rough neural network. Based on analyzing the demand of intelligent transportation system and the problems of the exiting recognition methods, the intelligent rapid recognition method of chaos in traffic flow was proposed. The principle and the structure of the system are briefly introduced. There are online recognition subsystem and offline recognition subsystem mainly. Normal methods are used in the offline recognition model. The online recognition model was established by using SVM rough neural network, which the wavelet packet energy features vector of the anterior time series of traffic flow in every training samples were used as input variables. The SVM rough neural network consist of three parts which generated by two hyperplanes that partition the universe. Rough neurons lie in the hidden layer and the hyperplanes are obtained by a method that is similar to SVM. The simulation result shows its correctness. And it can satisfy the real-time requirement of chaos recognition.