Design of a database-driven soft-sensor and its application to a control system

In industries, it is difficult to measure physical quantities such as product quality. In order to estimate it, soft sensors have been proposed. In recent years, the Just-In-Time (JIT) method has attracted attention. According to this method, the output is estimated by extracting data from the database similar to the current situation. On the other hand, a database-driven control system has been proposed in which the JIT is equipped with a learning algorithm. This method calculates the control parameters using a database and has the scheme to learn the control parameters to minimize the control error. This paper proposes a new design method for soft sensors which uses a database-driven approach. Specifically, this paper proposes a method to improve the estimation accuracy without modifying the stored past data by introducing a new modifier c into the learning algorithm. Furthermore, a control system is designed in which the estimated value using the soft sensor is fed back.