Deep neural network (DNN) has been applied in many fields and achieved great successes. However, DNN suffers from poor robustness for uncertainties because of its characteristic of the deterministic representation. To overcome this problem, a novel robust DNN (RDNN) is designed in this paper. First, a fuzzy denoising autoencoder (FDA) is developed to replace the general base-building unit in DNN. Then, the proposed RDNN is able to extract the robust representations to weaken the uncertainties. Second, a compact parameter strategy (CPS) is designed to reconstruct the parameters of FDA. Then, the computational burden of FDA can be alleviated to speed up the learning process. Third, an adaptive back-propagation (ABP) algorithm, with an adaptive learning rate strategy, is proposed to update the parameters of RDNN. Then, the performance of RDNN can be improved. Finally, the results on the benchmark problems and real applications demonstrate the effectiveness of RDNN.

中文翻译：

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使用模糊去噪自动编码器的鲁棒深度神经网络

深度神经网络（DNN）已应用于许多领域，并取得了巨大的成功。但是，由于DNN具有确定性表示的特征，因此不确定性的鲁棒性较差。为了克服这个问题，本文设计了一种新颖的鲁棒DNN（RDNN）。首先，开发了一种模糊去噪自动编码器（FDA）来代替DNN中的通用基础构建单元。然后，提出的RDNN能够提取鲁棒的表示，以减弱不确定性。其次，设计紧凑参数策略（CPS）来重建FDA的参数。然后，可以减轻FDA的计算负担，从而加快学习过程。第三，提出了一种带有自适应学习率策略的自适应反向传播算法，以更新RDNN的参数。然后，可以提高RDNN的性能。最后，关于基准问题和实际应用的结果证明了RDNN的有效性。