IOT and cloud computing based parallel implementation of optimized RBF neural network for loader automatic shift control

Abstract

One of the key issues in automatic shift control of V-type cyclical loaders is determining how to find the best gear for the current conditions according to a certain mapping relation, but this complex and nonlinear mapping is difficult to express by a mathematical relation. However, to solve such nonlinear problems, a radial basis function (RBF) neural network is the best choice. In this paper, a certain type of wheel loader is taken as the research object, and an RBF neural network algorithm based on an improved genetic algorithm (GA) optimization is proposed. The global search ability of the GA is improved by adaptively adjusting the crossover probability and mutation probability. The RBF neural network expansion coefficient is optimized by an improved GA. Using industrial IOT technology, an optimized RBF neural network based on Map-Reduce on a cloud computing cluster is designed. The diesel engine computer and transmission computer on the loader are integrated to achieve dual-processor distributed parallel data processing and calculation. Then the loader automatic variable speed control algorithm model of improved GA optimized RBF neural network based on IOT cloud computing is established. The network model is trained and simulated using real vehicle automatic shift test data. The simulation results show that the improved GA-RBF neural network algorithm can achieve a correct recognition rate of 97.92%. The error matrix norm reaches the minimum value when the algorithm is iterated to the 17th generation. The improved algorithm has the advantages of a high gear recognition rate, fast convergence speed and strong real-time shift performance and is an effective new shift control method. The test results show that the shift boost time is less than 0.15 s and has a certain gradient. Compared with the manual shift process performed in the past, some improvements are achieved in the optimal shift time, shift response speed and shift quality. Compared with the traditional single computer based on serial training RBF neural network learning algorithm, whether it is Great progress has been made in convergence speed, training time, recognition rate, and data processing capabilities. Through the simulation and test, the validity of the intelligent shift control method of the improved GA optimized RBF neural network based on IOT cloud computing is verified. It has better engineering application value.