Research On Traffic Pattern Recognition Technology Based On Incremental Naive Bayes Classifier

In the elevator group control system, the effectiveness of the analysis and processing elevator traffic flow data is an important factor that affects the performance of elevator group control system. Therefore, to carry out the accurate classification for the traffic situation inside the building and to adopt diverse elevator group control strategies in different traffic conditions can effectively improve the quality of service and performance index of elevators. At present, the most important method is the use of fuzzy neural networks for pattern recognition elevator traffic; however, because of large time-consuming in algorithm training, uncertain network structure, dependence on training data and poor generalization ability, this method makes the low accuracy in elevator traffic pattern recognition. Because of clear relationship between its condition attributes and decision-making in categories, higher speed of classification and stronger robustness, Naive Bayes has been successfully applied to many areas. When tagging a large number of categories with high price of the sample, combined with the incremental learning theory is an effective way to solve the problem. Therefore, how to integrate the Naive Bayesian classification and incremental learning algorithm and how to used the integrate model in an elevator traffic pattern recognition are subjects to be resolved.The paper includes the following work:①Analyzed the Bayesian network structure, characteristics and applications and builded a Naive Bayes classifier model.②Introduced an incremental classification model of Naive Bayes then put forward a new sequence learning algorithm to make up the deficiency of such models, this algorithm introduced a classifying loss weight coefficientλfor each training instance in order to calculate the total classifying loss. After introducing the coefficient, the classifier was optimized by fully utilizing the prior knowledge; by means of choosing reasonable learning sequence, positive influence of the mature data on classification was strengthened, classification precision was improved.③Naive Bayesian classifier model and improved incremental learning algorithm combining sequence and thus the establishment of a based on the incremental model of Naive Bayesian Classifier. In a careful analyzed of elevator traffic flow characteristics and patterns of the model based on the pattern recognition used in elevator traffic, through the collection of Elevator traffic flow data analyzed and feature extraction, used of MATLAB to carry out a simulation test, compared and analyzed the experimental results, the test results showed that the method of elevator traffic pattern recognition accuracy rate of 92.3%, compared to the identification of fuzzy neural network 90.6% had increased the accuracy, therefore the classification performance was more satisfactory.Through the definition and establishment of a Naive Bayesian Classifier-based and incremental learning algorithm for sequence classification integration model, this provides an effective solution for the elevator traffic pattern recognition. Naive Bayesian Classifier model feature vectors among the various components are relatively independent compared with the decision-making variable, which means the class variables are the only parent node for each of the attribute variables, this network structure reduce the complexity of constructing compared with the five-layer structure of fuzzy neural network, which makes the classification speed and classification performance excelled the fuzzy neural network. Because the passenger traffic of the office block is more obvious, the subject only studied the general office of the elevator traffic flow, such as shopping malls and uptown, which are not very obvious of passenger traffic ,this method whether suitable or not still need to be solved in future.