원문정보
초록
영어
This paper proposes an improved Markov prediction, according to the temperature control problem of hot blast stove alternate supply air in the operation of blast furnace operation, namely, implement clustering for waiting to be processed data, used kernel fuzzy c-means clustering based on the pairwise constraints. The supply air temperature of hot blast stove is seen as without aftereffect things in this method, introduce semi-supervised learning mechanism in traditional fuzzy clustering to deal with the basic data, and using the kernel effectiveness index improved the FCM algorithm. Experiments show that the improved clustering algorithm is superior to other algorithms in accuracy and performance, at the same time, the improved prediction model comparison with the traditional values of temperature prediction, which has obvious advantages in defined temperature range and the fit of the temperature value, the guiding significance was significantly enhanced in industrial field.
목차
1. Introduction
2. Mathematical Model of Dome Temperature
2.1. The Mathematical Model of Heat Storage
2.2. Optimization of Air Fuel Ratio Mathematical Model
3. Markov Operation Prediction
3.1. Basic Principle
3.2. Markov and Supply Air tTemperature
4. Kernel Fuzzy Clustering Algorithm based on Pairwise Constraints
4.1. Clustering Performance Index
4.2. Fuzzy c-means Clustering Algorithm
4.3. FCM Based on the Kernel
4.4. Target Function Adjustment
5. Experimental Analysis
6. Conclusion
References