Inversion of feedforward neural networks: algorithms and applications
Craig A. Jensen, Russell Reed, Robert J. Marks, M.A. El-Sharkawi, Jae-Byung Jung, Robert T. Miyamoto, G. M. Anderson, C.J. Eggen · Proceedings of the IEEE · 1999
There are many methods for performing neural network inversion. Multi-element evolutionary inversion procedures are capable of finding numerous inversion points simultaneously. Constrained neural network inversion requires that the inversion solution belong to one or more specified constraint sets. In many cases, iterating between the neural network inversion solution and the constraint set can successfully solve constrained inversion problems. This paper surveys existing methodologies for neural network inversion, which is illustrated by its use as a tool in query-based learning, sonar performance analysis, power system security assessment, control, and generation of codebook vectors.