In order to make full use of the driver’s long-term driving experience in the process of perception, interaction and vehicle control of road traffic information, a driving behavior rule extraction algorithm based on ...In order to make full use of the driver’s long-term driving experience in the process of perception, interaction and vehicle control of road traffic information, a driving behavior rule extraction algorithm based on artificial neural network interface(ANNI) and its integration is proposed. Firstly, based on the cognitive learning theory, the cognitive driving behavior model is established, and then the cognitive driving behavior is described and analyzed. Next, based on ANNI, the model and the rule extraction algorithm(ANNI-REA) are designed to explain not only the driving behavior but also the non-sequence. Rules have high fidelity and safety during driving without discretizing continuous input variables. The experimental results on the UCI standard data set and on the self-built driving behavior data set, show that the method is about 0.4% more accurate and about 10% less complex than the common C4.5-REA, Neuro-Rule and REFNE. Further, simulation experiments verify the correctness of the extracted driving rules and the effectiveness of the extraction based on cognitive driving behavior rules. In general, the several driving rules extracted fully reflect the execution mechanism of sequential activity of driving comprehensive cognition, which is of great significance for the traffic of mixed traffic flow under the network of vehicles and future research on unmanned driving.展开更多
In order to improve the ability of sharing and scheduling capability of English teaching resources, an improved algorithm for English text summarization is proposed based on Association semantic rules. The relative fe...In order to improve the ability of sharing and scheduling capability of English teaching resources, an improved algorithm for English text summarization is proposed based on Association semantic rules. The relative features are mined among English text phrases and sentences, the semantic relevance analysis and feature extraction of keywords in English abstract are realized, the association rules differentiation for English text summarization is obtained based on information theory, related semantic roles information in English Teaching Texts is mined. Text similarity feature is taken as the maximum difference component of two semantic association rule vectors, and combining semantic similarity information, the accurate extraction of English text Abstract is realized. The simulation results show that the method can extract the text summarization accurately, it has better convergence and precision performance in the extraction process.展开更多
基金Project(2017YFB0102503)supported by the National Key Research and Development Program of ChinaProjects(U1664258,51875255,61601203)supported by the National Natural Science Foundation of China+1 种基金Projects(DZXX-048,2018-TD-GDZB-022)supported by the Jiangsu Province’s Six Talent Peak,ChinaProject(18KJA580002)supported by Major Natural Science Research Project of Higher Learning in Jiangsu Province,China
文摘In order to make full use of the driver’s long-term driving experience in the process of perception, interaction and vehicle control of road traffic information, a driving behavior rule extraction algorithm based on artificial neural network interface(ANNI) and its integration is proposed. Firstly, based on the cognitive learning theory, the cognitive driving behavior model is established, and then the cognitive driving behavior is described and analyzed. Next, based on ANNI, the model and the rule extraction algorithm(ANNI-REA) are designed to explain not only the driving behavior but also the non-sequence. Rules have high fidelity and safety during driving without discretizing continuous input variables. The experimental results on the UCI standard data set and on the self-built driving behavior data set, show that the method is about 0.4% more accurate and about 10% less complex than the common C4.5-REA, Neuro-Rule and REFNE. Further, simulation experiments verify the correctness of the extracted driving rules and the effectiveness of the extraction based on cognitive driving behavior rules. In general, the several driving rules extracted fully reflect the execution mechanism of sequential activity of driving comprehensive cognition, which is of great significance for the traffic of mixed traffic flow under the network of vehicles and future research on unmanned driving.
文摘In order to improve the ability of sharing and scheduling capability of English teaching resources, an improved algorithm for English text summarization is proposed based on Association semantic rules. The relative features are mined among English text phrases and sentences, the semantic relevance analysis and feature extraction of keywords in English abstract are realized, the association rules differentiation for English text summarization is obtained based on information theory, related semantic roles information in English Teaching Texts is mined. Text similarity feature is taken as the maximum difference component of two semantic association rule vectors, and combining semantic similarity information, the accurate extraction of English text Abstract is realized. The simulation results show that the method can extract the text summarization accurately, it has better convergence and precision performance in the extraction process.
