Estimation of Distributed Generation Using Complex-Valued Network Inversion with Regularization
Network inversion has been studied as a neural network based solution of inverse problems. Complex-valued network inversion has been proposed as the extension of this inversion to the complex domain. Further, regularization is considered for solving ill-posed inverse problems. On the other hand, the estimation of the parameters of a distributed generation from observed data is a complex-valued inverse problem with ill-posedness. In this chapter, we propose the application of a complex-valued network inversion with regularization to the inverse estimation of a distributed generation.
KeywordsComplex-valued neural networks Distributed generation Ill-posed inverse problems Regularization
This work was supported in part by a Grant-in-Aid for Scientific Research #21700260 from the Japan Society for the Promotion of Science.
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