Training of Multi-layered Neural Network for Data Enlargement Processing Using an Activity Function  

作　　者：Betere Job Isaac Hiroshi Kinjo Kunihiko Nakazono Naoki Oshiro 

机构地区：[1]Mechanical Systems Engineering Course,Graduate School of Engineering and Science,University of the Ryukyus Senbaru 1, Nishihara,Okinawa 903-0213,Japan [2]Faculty of Engineering,University of the Ryukyus Senbaru 1,Nishihara,Okinawa 903-0213,Japan

出　　处：《Journal of Electrical Engineering》2019年第1期1-7,共7页电气工程（英文版）

摘　　要：In this paper, we present a study on activity functions for an MLNN (multi-layered neural network) and propose a suitable activity function for data enlargement processing. We have carefully studied the training performance of Sigmoid, ReLu, Leaky-ReLu and L & exp. activity functions for few inputs to multiple output training patterns. Our MLNNs model has L hidden layers with two or three inputs to four or six outputs data variations by BP (backpropagation) NN (neural network) training. We focused on the multi teacher training signals to investigate and evaluate the training performance in MLNNs to select the best and good activity function for data enlargement and hence could be applicable for image and signal processing (synaptic divergence) along with the proposed methods with convolution networks. We specifically used four activity functions from which we found out that L & exp. activity function can suite DENN (data enlargement neural network) training since it could give the highest percentage training abilities compared to the other activity functions of Sigmoid, ReLu and Leaky-ReLu during simulation and training of data in the network. And finally, we recommend L & exp. function to be good for MLNNs and may be applicable for signal processing of data and information enlargement because of its performance training characteristics with multiple teacher training patterns using original generated data and hence can be tried with CNN (convolution neural networks) of image processing.

关 键 词：DATA ENLARGEMENT PROCESSING MLNN ACTIVITY FUNCTION multi teacher TRAINING signals BP NN CNN 

分 类 号：TM[电气工程]