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.

神经网络的反转旨在找到给定目标输出的最优输入变量，并广泛应用于工业领域，例如优化制造设施中复杂系统的控制变量。为了使用标准的一阶优化技术获得最佳输入，输入变量的正确初始化至关重要。本文提出了一种新的基于k-近邻（k-NN）方法。所提出的方法在训练数据集中找到导致输出接近目标输出的输入，并将它们组合以形成初始输入变量。在玩具数据集上进行的实验表明，我们的方法优于随机初始化。此外，我们还介绍了有关建筑物中供暖，通风和空调（HVAC）系统的功率调度的详尽案例研究，以支持该算法的有效性。