Input initialization for inversion of neural networks using k-nearest neighbor approach
SCIE
SCOPUS
- Title
- Input initialization for inversion of neural networks using k-nearest neighbor approach
- Authors
- Hwanjo Yu; Seongbo Jang; Ye-Eun Jang; Young-Jin Kim
- Date Issued
- 2020-05
- Publisher
- ELSEVIER SCIENCE INC
- 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. (C) 2020 Elsevier Inc. All rights reserved.
- Keywords
- FREQUENCY REGULATION
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/100876
- DOI
- 10.1016/j.ins.2020.01.041
- ISSN
- 0020-0255
- Article Type
- Article
- Citation
- INFORMATION SCIENCES, vol. 519, page. 229 - 242, 2020-05
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