Inversion of feedforward neural networks: algorithms and applications

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....

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Bibliographic Details
Published in:Proceedings of the IEEE 1999, Vol.87 (9), p.1536-1549
Main Authors: Jensen, C.A., Reed, R.D., Marks, R.J., El-Sharkawi, M.A., Jae-Byung Jung, Miyamoto, R.T., Anderson, G.M., Eggen, C.J.
Format: Article
Language:English
Subjects:
Assessments
Constraints
Control systems
Feedforward neural networks
Inversions
Mathematical analysis
Multi-layer neural network
Neural networks
Neurons
Performance analysis
Power generation
Power system security
Sonar
Training data
Vectors (mathematics)
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Summary: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.
ISSN:0018-9219
DOI:10.1109/5.784232