A learning method for solving inverse problems of static systems

The problem of computing the input value realizing the desired output value of the target system is called the inverse problem. The method that uses an acquired inverse model of the target system by learning is popular. However, acquisition of the inverse model has a number of drawbacks. In this paper, a generalized inverse model with output feedback using the learned inverse model of the linearized model of the target system is proposed. Further, two possible configurations of the generalized inverse model are presented. The performance of the proposed method and the effect of the learning are shown by numerical simulations. By using a random search technique for the initial value, the proposed method obtains precise solutions for inverse problems.