摘要
In order to accurately and quickly identify the safety status pattern of coalmines,a new safety status pattern recognition method based on the extension neural network (ENN) was proposed,and the design of structure of network,the rationale of recognition algorithm and the performance of proposed method were discussed in detail.The safety status pattern recognition problem of coalmines can be regard as a classification problem whose features are defined in a range,so using the ENN is most appropriate for this problem.The ENN-based recognition method can use a novel extension distance to measure the similarity between the object to be recognized and the class centers.To demonstrate the effectiveness of the proposed method,a real-world application on the geological safety status pattern recognition of coalmines was tested.Comparative experiments with existing method and other traditional ANN-based methods were conducted.The experimental results show that the proposed ENN-based recognition method can identify the safety status pattern of coalmines accurately with shorter learning time and simpler structure.The experimental results also confirm that the proposed method has a better performance in recognition accuracy,generalization ability and fault-tolerant ability,which are very useful in recognizing the safety status pattern in the process of coal production.
In order to accurately and quickly identify the safety status pattern of coal mines, a new safety status pattern recognition method based on the extension neural network （ENN） was proposed, and the design of structure of network, the rationale of recognition algorithm and the performance of proposed method were discussed in detail. The safety status pattern recognition problem of coal mines can be regard as a classification problem whose features are defined in a range, so using the ENN is most appropriate for this problem. The ENN-based recognition method can use a novel extension distance to measure the similarity between the object to be recognized and the class centers. To demonstrate the effectiveness of the proposed method, a real-world application on the geological safety status pattern recognition of coal mines was tested. Comparative experiments with existing method and other traditional ANN-based methods were conducted. The experimental results show that the proposed ENN-based recognition method can identify the safety status pattern of coal mines accurately with shorter learning time and simpler structure. The experimental results also confirm that the proposed method has a better performance in recognition accuracy, generalization ability and fault-tolerant ability, which are very useful in recognizing the safety status pattern in the process of coal production.
基金
Project(107021) supported by the Key Foundation of Chinese Ministry of Education
Project(2009643013) supported by China Scholarship Fund
