复值多层感知器如何预测确定性混沌的行为

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI:10.1109/IJCNN.2016.7727736

Seiya Satoh, R. Nakano

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引用次数: 0

摘要

复值多层感知器具有表征复杂周期的能力。我们使用了一种非常强大的学习方法，称为C-SSF来学习复杂值多层感知器。C-SSF通过连续学习找到了一系列优秀的解决方案。在确定性混沌中，长期预测被认为是不可能的。我们将C-SSF应用于两种确定性混沌，并评估了C-SSF的学习和预测性能。

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How complex-valued multilayer perceptron can predict the behavior of deterministic chaos

A complex-valued multilayer perceptron has the capability to represent complicated periodicity. We employ a very powerful learning method called C-SSF for learning a complex-valued multilayer perceptron. C-SSF finds a series of excellent solutions through successive learning. In deterministic chaos, long-term prediction is considered impossible. We apply C-SSF to two kinds of deterministic chaos and evaluate the learning and prediction performance of C-SSF.

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2016 International Joint Conference on Neural Networks (IJCNN)

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