Inversion of feedforward neural networks: Algorithms and applications

Feedforward layered perception neural networks seek to capture a system mapping inferred by training data. A proper ly trained neural network is nor only capable of mimicking the process responsible for generating the training data, but the inverse process as well. Neural network inversion procedures seek to find one or more input values that produce a desired output response for a fixed set of synaptic weights. There ale many methods for performing neural network inversion. Multi-element evolutionary inversion procedures ale capable of finding numerous inversion points simultaneously. Constrained neural network inversion requires that the inversion solution belong to one or mole 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 or 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.