A real-time monitoring method using random projection and k-nearest neighbor rule for batch process

Abstract

As an important production method, the batch process is complex and flexible. Moreover, the modeling complexity and the spatial complexity of the storage model are higher, and the monitoring of the actual batch process is more difficult. To address this problem, this article proposes a fault detection method based on random projection, K-means clustering, and the k-nearest neighbor algorithm. First, a multiperiod division method is put forward based on the random projection and the K-means clustering algorithm. This reduces the computational complexity while ensuring the fault detection performance of the algorithm. Second, a real-time monitoring model is established based on each sub-period data using the k-nearest neighbor method to realize online monitoring of the batch production process. According to the premise that the fault detection performance is approximately equal, the proposed method reduces the complexity and computation of the model and realizes the real-time demand of fault detection.

Research on autonomous collision avoidance of merchant ship based on inverse reinforcement learning

“…As a search process of reinforcement learning, the noise CE method needs to search the optimal policy on the premise of defining the weight matrix of the reward function. The projection method 27,33 is used in this study to obtain the approximated reward of expert policy using the weight matrix W as the medium. Firstly, the state feature expectations of expert demonstration samples are calculated…”