ABSTRACT

Research on machine deep learning with fuzzy neural network (FNN) is one hot topic in the Artificial Intelligent (AI) domain. In order to support the application of the IoT (Internet of Things) and make use of these image data to get perfect image reasonably and efficiently, it is necessary to fuse these sensed data, therefore the multiple-sensors’ image aggregation becomes a key technology. In this paper, novel FNN-based machine deep learning approach for image aggregation in application of the IoT is proposed. When this approach is done, dynamic learning from eigenvalue transition example can improve traditional learning approach based on static eigenvalue of example. And the neural network is used to be demonstrated its unique superiority of image understanding. FNN-based machine deep learning approach can learn from dynamic eigenvalues, the change of data can be learned and the varieties of the eigenvalue can be understood and remembered. The relative experiments have shown the designed approach for image aggregation is fast and effective, and it can be adapted for the many image applications of the IoT.

摘要

基于模糊神经网络（FNN）的机器深度学习研究是人工智能（AI）领域的热门话题之一。为了支持IoT（物联网）的应用，合理、高效地利用这些图像数据得到完美的图像，就需要对这些感知数据进行融合，因此多传感器的图像聚合成为关键技术. 在本文中，提出了一种新的基于 FNN 的机器深度学习方法，用于物联网应用中的图像聚合。当这种方法完成时，从特征值转换示例的动态学习可以改进基于示例的静态特征值的传统学习方法。而神经网络则被用来展示其独特的图像理解优越性。基于 FNN 的机器深度学习方法可以从动态特征值中学习，可以学习数据的变化，理解和记住特征值的变化。相关实验表明，所设计的图像聚合方法快速有效，可适用于物联网的多种图像应用。