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.
翻译:本文为完全或部分建模人工神经网络(ANN)的反函数提供了一个创新的生成神经网络架构。 模拟一个ANN的完整反函数涉及生成与预期输出相应的所有特性的值。 另一方面, 部分建模反函数意味着生成一组特性的值并固定其余特性值。 特性集生成是人工神经网络的关键步骤, 可用于工程和科学方面的若干实际应用。 拟议的Oracle 引导合成神经网络( GGN 称OGN ) 具有处理多种特性生成问题的灵活性。 一般来说, ANN 的完整反函数涉及生成与预期输出输出对应的所有特性的值。 另一方面, 部分建模反函数意味着生成一组特性的值, 和其余特性值。 当生成的特性矢量被反馈到 ANNN, 预测的目标值将接近预定的目标值。 因此, GOGN 结构能够绘制由前方代表的功能的反向函数。 此外, ANNGNO 还可以预测基于特定特性变量的目标值, 也能够预测基于特定特性变现的轨道功能 。 使一个驱动的硬性轨道 。 运行成为一个驱动的硬度 。 。 。 将一个硬度 向一个硬度 。 。 将一个硬值 将一个硬值 向一个硬值 向一个硬值 向一个硬值 向一个硬值 运行到一个硬值 。