Regularization for complex-valued network inversion
Seisho Fukami, Takehiko Ogawa, Hajime Kanada · 2008
Network inversion solves inverse problems to estimate cause from result using a multilayer neural network. The original network inversion has been applied to usual multilayer neural network with real-valued inputs and outputs. The solution by a neural network with complex-valued inputs and outputs is necessary for the general inverse problems including complex numbers. The complex-valued network inversion method has been proposed to solve the inverse problems with complex numbers. In general, there is a problem attributable to the ill-posedness on the inverse problems. To solve the ill-posedness, the regularization is used to add some conditions on the solution. In this study, we propose to introduce the regularization to the complex-valued network inversion.