Input initialization for inversion of neural networks using k-nearest neighbor approach

Abstract

Inversion of neural networks aims to find optimal input variables given a target output, and is widely applicable in an industrial field such as optimizing control variables of complex systems in manufacturing facilities. To achieve optimal inputs using a standard first-order optimization technique, proper initialization of input variables is essential. This paper presents a new initialization method for input variables of neural networks based on k-nearest neighbor (k-NN) approach. The proposed method finds inputs which resulted in an output close to a target output in a training dataset, and combine them to form initial input variables. Experiments on a toy dataset demonstrate that our method outperforms random initialization. Also, we introduce an exhaustive case study on power scheduling of a heating, ventilation, and air conditioning (HVAC) system in a building to support the effectiveness of the algorithm.