Evaluation Method of Human-Machine Interface of Virtual Meter Based on RBF Network

A novel subjective evaluation approach for evaluating the human-machine interface of virtual meters based on RBF neural network as well as the evaluation indexes were proposed. By using the self-organizing,self-learning and self-adapting properties of RBF neural networks,the regularity of subjective evaluation indexes weight concealed in the training data could be learned by means of RBF neural networks automatically adjusting indexes weight of subjective evaluation,and therefore the influence of randomicity could be overcome. In order to validate the proposed method,a human-machine interface of virtual bargraph meters was developed and the subjective evaluation model of virtual bargraph meter was established. Error analysis of the subjective evaluation model for three groups of virtual bargraph meters was performed by using 50,75,and 100 training samples,respectively. Analysis results show that the subjective evaluation model by using 75 training samples is of satisfied accuracy.