Novel FNN-based machine deep learning approach for image aggregation in application of the IoT

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.