Neural Computation
The nature of statistical learning theory
The nature of statistical learning theory
A Variational Approach to Robust Regression
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Intelligent control of a stepping motor drive using an adaptive neuro-fuzzy inference system
Information Sciences—Informatics and Computer Science: An International Journal
Engineering Applications of Artificial Intelligence
Airbag controller designed by adaptive-network-based fuzzy inference system (ANFIS)
Fuzzy Sets and Systems
The evidence framework applied to classification networks
Neural Computation
Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine
Expert Systems with Applications: An International Journal
Modelling of a new solar air heater through least-squares support vector machines
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Neural modeling helps the BOS process to achieve aimed end-point conditions in liquid steel
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence
Neural intelligent control for a steel plant
IEEE Transactions on Neural Networks
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
Journal of Control Science and Engineering - Special issue on Advanced Control in Micro-/Nanosystems
Hi-index | 12.05 |
This study concerns with the control of basic oxygen furnace (BOF) steelmaking process and proposes a dynamic control model based on adaptive-network-based fuzzy inference system (ANFIS) and robust relevance vector machine (RRVM). The model aims to control the second blow period of BOF steelmaking and consists of two parts, the first of which is to calculate the values of control variables, viz., the amounts of oxygen and coolant requirement, and the other is to predict the endpoint carbon content and temperature of molten steel. In the first part, an ANFIS classifier is primarily constructed to determine whether coolant should be added or not, then an ANFIS regression model is utilized to calculate the amounts of oxygen and coolant. In the second part, a novel robust relevance vector machine is presented to predict the endpoint. RRVM solves the problem of sensitivity to outlier characteristic of classical relevance vector machine, thus obtaining higher prediction accuracy. The key idea of the proposed RRVM is to introduce individual noise variance coefficient to each training sample. In the process of training, the noise variance coefficients of outliers gradually decrease so as to reduce the impact of outliers and improve the robustness of the model. Simulations on industrial data show that the proposed dynamic control model yields good results on the oxygen and coolant calculation as well as endpoint prediction. It is promising to be utilized in practical BOF steelmaking process.