Solving inverse problem in reheater system modeling

Human perception of uncertainties plays an important role in handling parameter problems in multivariable system. Often, human experts describe the decision parameters of the systems through vague and uncertain statements. As a result, achieving a precise model of these systems become increasingly difficult and challenging especially in solving inverse problems in system modeling. The inverse problem is one type of ill-conditioned problems whereby the desired responses are given and a model is used to estimate the input parameters. Thus, this paper discusses a fuzzy state space approach for solving the inverse problem of a reheater system of the combined cycle power plant. A state space model of a reheater system with two input parameters and two output parameters is considered. Here, the uncertainties in the parameters are represented by fuzzy number, with its membership obtained from the human experts. The optimal combination of the input parameters is determined by using the Modified Optimized Defuzzified Value Theorem. These values are compared with those obtained using the forward simulation approach, highlighting some distinguish features of the fuzzy state space approach.