随机单隐层神经网络的分布参数估计
Distributed Parameter Estimation in Randomized One-hidden-layer Neural Networks
摘要：
研究了随机单隐层神经网络的分布参数估计问题。一组代理顺序地接收对它们仅部分可观察的未知参数的测量。在本文中，我们提出了一个完全分布式的估计算法，代理交换本地估计与他们的邻居共同识别参数的真实值。我们证明了这种分布式更新提供了未知参数的渐近无偏估计，即，期望全局误差的一阶矩渐近收敛于零。我们进一步分析所提出的估计方案的效率，通过建立一个渐近上界的方差的全局误差。将我们的方法应用于与家电能源预测相关的真实数据集，我们观察到我们的实证研究结果验证了理论结果。
英文摘要：
This paper addresses distributed parameter estimation in randomized one-hidden-layer neural networks. A group of agents sequentially receive measurements of an unknown parameter that is only partially observable to them. In this paper, we present a fully distributed estimation algorithm where agents exchange local estimates with their neighbors to collectively identify the true value of the parameter. We prove that this distributed update provides an asymptotically unbiased estimator of the unknown parameter, i.e., the first moment of the expected global error converges to zero asymptotically. We further analyze the efficiency of the proposed estimation scheme by establishing an asymptotic upper bound on the variance of the global error. Applying our method to a real-world dataset related to appliances energy prediction, we observe that our empirical findings verify the theoretical results.
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