Dump Truck Fault’s Short-Term Forecasting Based on the Multi-agent Adaptive Fuzzy Neuronet

Abstract

Dump truck fault’s short-term forecasting is the important step for solving real-time fleet dispatching tasks and to provide reliable, efficient and safe open-pit mining. This paper presents a multi-agent adaptive fuzzy neuronet for dump truck fault’s short-term forecasts. The agents of the multi-agent adaptive fuzzy neuronet are fulfilled based on recurrent networks. An automatic determination of the optimal architecture’s parameters of a neuronet is the most critical task. In order to train the effective multi-agent adaptive fuzzy neuronet we use algorithm, in which the multi-dimensional Particle Swarm Optimization is combined with the Levenberg-Marquardt algorithm. The multi-dimensional Particle Swarm Optimization is first applied to globally optimize the multi-agent adaptive fuzzy neuronet’s structure, and then Levenberg-Marquardt is used to speed up convergence process. The simulation results show that proposed training algorithm outperforms multi-dimensional Particle Swarm Optimization and Levenberg-Marquardt algorithm in training the effective multi-agent adaptive fuzzy neuronet for dump truck fault’s short-term forecasts.