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Input Initialization for Inversion of Neural Networks Using k-Nearest Neighbor Approach

Title
Input Initialization for Inversion of Neural Networks Using k-Nearest Neighbor Approach
Authors
Hwanjo YuSeongbo JangYe-Eun JangYoung-Jin Kim
Date Issued
May-2020
Publisher
Elsevier
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.
URI
http://oasis.postech.ac.kr/handle/2014.oak/100876
ISSN
0020-0255
Article Type
Article
Citation
Information Sciences, vol. 519, page. 229 - 242, 2020-05
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 YU, HWANJO
Dept of Computer Science & Enginrg
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