文章基本信息

标题：UNCERTAIN INPUT SELECTION MODEL FOR NEURON
本地全文：下载
作者：ZULFIAN AZMI ; MUHAMMAD ZARLIS ; HERMAN MAWENGKANG 等
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2019
卷号：97
期号：21
页码：2982-2993
出版社：Journal of Theoretical and Applied
摘要：The application of Artificial Neural Network model does not provide optimal results in learning with the large quantity of inputs and real time. The input stated in matrix with plenty of quantity makes the process in pattern recognition getting slow. A model is required to minimize input for training and to recognize patterns faster. Input recognition is required to know the special characteristics of inputs which may represent all inputs using a model. In addition, input recognition is necessary to know the inputs with the binary value not only 1 and 0, but it may be with the value in between. To know uncertain inputs, it is conducted by determining the degree of membership of each variable. And, for each selection of input, it can be done by declaring it as a row vector and by calculating the euclidean distance between each row vector. Furthermore, the selected input may represent input for training. Training is carried out with input variables consisting of dissolved oxygen, water pH, salinity and water temperature to determine the quality of water. With the model algorithm which is called as Uncertain Input Selection Model for neurons, it helps to accelerate training in the system to determine the water wheel can rotate.
关键词：Selection; Input; Uncertain; Euclidean