Compact Incremental Random Weight Network for Estimating the Underground Airflow Quantity

Optimal operation of an actual mine main fan switchover process relies heavily on a good measurement of underground airflow quantity (UAQ). However, real-time measuring the UAQ is difficult using conventional measurement techniques. In this article, a novel randomized learning model, named compact incremental random weight network (CIRWN), is proposed to estimate the UAQ. Since the hidden parameters of the original IRWN are generated in a fixed scope with a random manner, which is prone to create redundant hidden nodes, a CIRWN with new inequality constraints is proposed. The inequality constraints have several attractive properties, including dynamically guiding the generation of hidden parameters, effectively enhancing the convergence rate, and successfully establishing a universal approximator. Experiments using four benchmark datasets and an industrial dataset show that the established model possesses a more compact network structure and better modeling accuracy as well as faster convergence rate compared with other methods.