Parameter Prediction for Lorenz Attractor by using Deep Neural Network

Nurnajmin Qasrina Ann, Dwi Pebrianti, Mohammad Fadhil Abas, Luhur Bayuaji, Mohammad Syafrullah


Nowadays, most modern deep learning models are based on artificial neural networks. This research presents Deep Neural Network to learn the database, which consists of high precision, a strange Lorenz attractor. Lorenz system is one of the simple chaotic systems, which is a nonlinear and characterized by an unstable dynamic behavior. The research aims to predict the parameter of a strange Lorenz attractor either yes or not. The primary method implemented in this paper is the Deep Neural Network by using Phyton Keras library. For the neural network, the different number of hidden layers are used to compare the accuracy of the system prediction. A set of data is used as the input of the neural network, while for the output part, the accuracy of prediction data is expected. As a result, the accuracy of the testing result shows that 100% correct prediction can be achieved when using the training data. Meanwhile, only 60% correct prediction is achieved for the new random data.


Chaos System; Deep Learning; Artificial Neural Network; Lorenz Attractor; Prediction

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Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
ISSN 2089-3272

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This work is licensed under a Creative Commons Attribution 4.0 International License.

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