Intelligent loading system for bulk materials based on FWA-RFN

Abstract

In order to solve the problems of serious unbalanced loading and large errors during the loading of bulk materials in coal mines, an intelligent loading system for bulk materials based on the fireworks algorithm (FWA) optimized recursive fuzzy neural network (RFNN) is proposed. By comparing the measured value and the set value of train carriage speed, the deviation is obtained as the input of RFNN controller. The deviation is processed by RFNN controller for fuzzification, dynamic memory adjustment and defuzzification. FWA is used to optimize RFNN weight so that RFNN controller can self-adaptively output the corrected control parameters. The traction motor frequency is obtained by the bulk material loading metering model, the material quality, material height and distance traveled during carriages loading collected by each sensor and the control parameters output from RFNN controller. Furthermore, the traction motor speed is changed so as to adjust the train carriage speed and realize the unbalanced loading of bulk materials. It has been proved that RFNN controller optimized by FWA can quickly adjust the carriages speed and keep the speed stable so as to meet the requirements of distributed and balanced loading of multiple carriages and improve the loading accuracy at the same time.

Keywords

• bulk material loading
• unbalanced loading
• fireworks algorithm
• recursive fuzzy neural network