A neuro-fuzzy adaptive control strategy for refuse incineration plants
Fuzzy Sets and Systems - Special issue on industrial applications
Intelligent control of a stepping motor drive using an adaptive neuro-fuzzy inference system
Information Sciences—Informatics and Computer Science: An International Journal
Neural Networks, Fuzzy Logic and Genetic Algorithms
Neural Networks, Fuzzy Logic and Genetic Algorithms
A neuro-fuzzy controller for speed control of a permanent magnet synchronous motor drive
Expert Systems with Applications: An International Journal
Airbag controller designed by adaptive-network-based fuzzy inference system (ANFIS)
Fuzzy Sets and Systems
Computers & Mathematics with Applications
Hybrid MATLAB and LabVIEW with neural network to implement a SCADA system of AC servo motor
Advances in Engineering Software
Direct adaptive neural flight control system for an unstable unmanned aircraft
Applied Soft Computing
Online estimation of electric arc furnace tap temperature by using fuzzy neural networks
Engineering Applications of Artificial Intelligence
Fuzzy adaptive output feedback control for MIMO nonlinear systems
Fuzzy Sets and Systems
Paper: Electric arc furnace modelling and control
Automatica (Journal of IFAC)
Training feedforward networks with the Marquardt algorithm
IEEE Transactions on Neural Networks
Survey paper: A survey on industrial applications of fuzzy control
Computers in Industry
Dynamic control model of BOF steelmaking process based on ANFIS and robust relevance vector machine
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
This paper illustrates the control strategies of an Electric Arc Furnace. It involves the prediction of the control action which aids in reduction of carbon, manganese and other impurities from the in-process molten steel. Predictive models using Artificial Neural Networks (ANN) with Bayesian Regularization and Adaptive Neuro Fuzzy Inference System (ANFIS) were developed. The control action is the amount of oxygen to be lanced at different sampling instants. The predictive models were constructed based on the values of the individual chemical constituents of the collected molten samples. Two control strategies were devised: one with full sampling and the other with limited or reduced sampling. For the full sampling case two predictive models were devised separately with ANN with Bayesian Regularization and ANFIS. For the limited sampling strategy a combination of ANN with Bayesian Regularization and ANFIS were employed. For full sampling strategy, ANFIS model performs better than ANN. The application of the limited sampling strategy gave satisfactory Mean Percentage Error (MPE) thereby justifying its practical implementation. The main advantage of reduced or limited sampling is that it helps in the reduction of cost, time and manpower associated the sample collection and its subsequent analysis.