Abstract
The gradient descent momentum and adaptive learning rate TD-DBP algorithm can improve the training speed and stability of Elman network effectively. BP algorithm is the typical supervised learning algorithm, so neural network cannot be trained on-line by it. For this reason, a new algorithm (TD-DBP), which was composed of temporal difference (TD) method and dynamic BP algorithm (DBP), was proposed to overcome the restriction. TD-DBP algorithm can make Elman network train on-line incrementally. Using the collected real time data, the modified TD-DBP algorithm was able to realize direct multi-step predictions for vertical displacement of wave compensating platform.
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© 2012 Springer-Verlag GmbH Berlin Heidelberg
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Zeng, Z. (2012). Multi-step Predictions Based on TD-DBP ELMAN Neural Network for Wave Compensating Platform. In: Jin, D., Lin, S. (eds) Advances in Computer Science and Information Engineering. Advances in Intelligent and Soft Computing, vol 169. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30223-7_67
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DOI: https://doi.org/10.1007/978-3-642-30223-7_67
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