Abstract
A new model based on improved ant colony algorithm (ACA) and backpropagation (BP) is proposed to predict Silicon content of hot metal in blast furnace. BP algorithm has been widely used in training artificial neural network (ANN), which is an outstanding model to predict Silicon content. BP algorithm has many attractive features, such as adaptive learning, self- organism, and fault tolerant ability. All of them make BP one of the most successful algorithms in various fields. But BP suffers from relatively slow convergence speed, extensive computations and possible divergence for certain conditions. As a new bionic algorithm, the improved ACA has gained very good performance in solving traveling salesman problem (TSP) and other optimization problems. Its properties such as distributed computation, heuristic searching and robustness have well conquered the long convergence speed and premature problem, which are the main deficiencies of BP algorithm. Experiments show the model proposed has good performance in predicting Silicon content of hot metal.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
