원문정보
초록
영어
Incorporating prior knowledge (PK) into learning methods is an effective means to improve learning performance. On the bases of requirements of engineering practice and the characteristics of knowledge representation of extension neural network (ENN), with the purpose of further improving the performance of ENN in engineering practice, a prior-knowledge-based ENN (PKENN) recognition method is proposed and applied in the application of safety status pattern recognition of coal mines in this paper. The PKENN recognition method effectively combines domain knowledge with training data set. The prior knowledge can provide additional information about the classical domain of characteristic vector that may compensate for the low quality of training data in a complex application environment. This method can set the initial weights of ENN, guide the learning of ENN and alleviate the learning burden. To demonstrate the validity and effectiveness of the proposed method, a real-world application on the geological safety status pattern recognition of coal mines is tested. Comparative experiments with existing methods and other ANN-based methods are conducted. The experimental results show that the proposed PKENN recognition method has a better performance.
목차
1. Introduction
2. Theoretical Background
2.1. Outline of Extension Theory
2.2. Extension Neural Network
3. The Mechanisms of PKENN-based Pattern Recognition Method
3.1. Basic Design Idea of PKENN
3.2. Knowledge Representation of ENN
3.3. Advantages of PKENN-based Recognition Method
3.4. Structure Design of ENN
3.5. PKENN-based Recognition Algorithm
4. Experimental Results and Discussion
4.1. Background of the Application
4.2. Comparison with Existing Methods
4.3. Comparison with Other ANN-based Methods
4.4. Tests of Error-containing Data
5. Conclusions
ACKNOWLEDGEMENTS
References