Usability Evaluation Research on Operation and Monitoring Interface of Main Control Room

Deciding the evaluation index weight is a key technique in estimating human factor design of operation and monitoring interfaces (OMI). It is very hard to avoid the influences caused by the individualities of valuators and the randomicity factors by using the traditional OMI evaluation methods. An RBF networks based subjective evaluation method is proposed in this paper, which has the properties of self-organizing, self-learning, self-adapting. In addition, because the RBF networks can be trained to study and learn the regularity of index weights of subjective evaluation concealed in the training data, and therefore the influence of randomicity factors can be overcome by means of RBF networks automatically adjusting index weights of subjective evaluation. The subjective evaluation indexes and models of OMI are established. The number of samples, the value of spread, the accuracy of RBF subjective evaluation network model, and the relationship between them are researched Error analyses of OMI subjective evaluation model based on RBF network are carried out by using different training samples, respectively. The analysis results show that the OMI subjective evaluation model by using 80 training samples is of satisfied accuracy.