Hidden behavior prediction of complex system based on time-delay belief rule base forecasting model

The hidden belief rule base model (HBRB) which can utilize both hybrid expert's experience and experimental data has become a useful method for hidden behavior forecasting of complex system in many applications. But the traditional HBRB model is one-step forecasting model, which means that it can only utilize the limited information in very short time instant. In fact, one-step forecasting method is incomplete because the future behavior of a complex system is generated through multiple historical stats in different time instant. Therefore, a time-delay hidden BRB forecasting model (THBRB) is designed, where the input with multiple time instant of the HBRB model is considered, and the corresponding reasoning process is also designed. Further, an optimization method based on projection covariance matrix adaption evolution strategy (P-CMA-ES) algorithm is used to train the initial THBRB model. A case study is established to prove the advantage of the proposed method, and the experiment results show that the proposed THBRB model can predict the future hidden behavior of complex system effectively. (C) 2020 Elsevier B.V. All rights reserved.