  This paper presents a novel Generative Neural Network Architecture for
modelling the inverse function of an Artificial Neural Network (ANN) either
completely or partially. Modelling the complete inverse function of an ANN
involves generating the values of all features that corresponds to a desired
output. On the other hand, partially modelling the inverse function means
generating the values of a subset of features and fixing the remaining feature
values. The feature set generation is a critical step for artificial neural
networks, useful in several practical applications in engineering and science.
The proposed Oracle Guided Generative Neural Network, dubbed as OGGN, is
flexible to handle a variety of feature generation problems. In general, an ANN
is able to predict the target values based on given feature vectors. The OGGN
architecture enables to generate feature vectors given the predetermined target
values of an ANN. When generated feature vectors are fed to the forward ANN,
the target value predicted by ANN will be close to the predetermined target
values. Therefore, the OGGN architecture is able to map, inverse function of
the function represented by forward ANN. Besides, there is another important
contribution of this work. This paper also introduces a new class of functions,
defined as constraint functions. The constraint functions enable a neural
network to investigate a given local space for a longer period of time. Thus,
enabling to find a local optimum of the loss function apart from just being
able to find the global optimum. OGGN can also be adapted to solve a system of
polynomial equations in many variables. The experiments on synthetic datasets
validate the effectiveness of OGGN on various use cases.
