文章基本信息

标题：Weather Prediction using Stochastic Weight Updation with Reinforced Learning
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作者：Siji Chacko ; Sam G Benjamin
期刊名称：International Journal of Computer Science and Network
印刷版ISSN：2277-5420
出版年度：2019
卷号：8
期号：3
页码：217-221
出版社：IJCSN publisher
摘要：Humans always want to know what will happen in the next days or future. Now a days, prediction is most important that gives a knowledge about what will happen in the future. Today, weather is variable in nature so its prediction has become an important field of research. Several traditional methods are used for weather prediction. But they have their limitations for handling and predicting accurately. Predicting weather using artificial neural network (ANN) gives better results than the traditional methods. Stochastic weight updation with reinforced learning is used for the learning of neural network. There are several advantages by using this method, its implementation is simple for network topology and it allow better parallelization of the backpropagation algorithm. In stochastic weight update method, weights are selected according to certain threshold for updation instead of updating all weights. Reinforced learning is used for balanced weight updation. Here for attaining an optimized and accurate result it is used along with the stochastic weight updation.
关键词：Artificial neural network (ANN); Reinforced learning; stochastic weight updation