基于神经网络和ANFIS的Java海海上天气预报设计提高预报精度

W. Dhanistha, W. Wardhana, Mufidatul Islamiyah
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引用次数: 0

摘要

海上运输是印尼的主要交通方式,因为印尼是由成千上万的岛屿组成的,所以要连接岛屿之间,就需要海上运输。海浪与海洋密切相关,负面的海浪会危及航运。造成海上事故的因素之一是自然灾害,即大浪。为了最大限度地减少因大浪造成的事故,可以使用神经网络算法在未来几小时内进行海浪预测。选择神经网络是因为它在处理非线性系统的输入输出数据方面具有优势。其优点是选择神经网络作为波高预测算法。ANFIS是一种神经网络与模糊人工智能相结合的发展算法。ANFIS预测波高的能力并不亚于神经网络,这是因为它是神经网络和模糊的结合。希望通过本文的研究,可以比较哪一种算法在预测波高方面效果更好。
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Maritime Weather Predictor Design Based Neural Network and ANFIS to an Increase in Accuracy in the Java Sea
: Sea transportation is a mainstay transportation in Indonesia, it is because Indonesia consists of thousands of islands, so that, to connect between islands, sea transportation is needed. Waves are very closely related to the sea, waves that are negative that is waves that can endanger shipping. One of the factors causing sea accidents is natural disasters, namely high waves. To minimize accidents due to high waves, wave predictions can be made in the hours to come using the neural network algorithm. Neural network was chosen because of its advantages in processing system input-output data even though the system is nonlinear. The advantage is that the neural network is chosen as a wave height predictor algorithm. ANFIS is an algorithm for the development of a combination of neural networks and fuzzy artificial intelligence. The ability of ANFIS to predict wave heights is no less good with neural networks, it is because ANFIS is a combination of neural network and fuzzy. It is hoped that by doing this research it can compare which algorithm is better in predicting wave heights.
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