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Abstract: The paper introduces a new type of evolving fuzzy neural networks (EFuNNs), denoted as mEFuNNs, for on-line learning and their applications for dynamic time series analysis and prediction. mEFuNNs evolve through incremental, hybrid (supervised/unsupervised), on-line learning, like the EFuNNs. They can accommodate new input data, including new features, new classes, etc. through local element tuning. New connections and new neurons are created during the operation of the system. At each time... (Update)
Context of citations to this paper: More
...fifth layer represents the real values for the output variables. EFuNN evolving algorithm used in our experimentation was adapted from [2]. Figure 2. Architecture of EFuNN The neuro fuzzy network is trained using the trend patterns of the different stock values. The difference...
...algorithm. Some of the major works in this area are GARIC [11] FALCON [9] ANFIS [4] NEFCON [9] FUN [7] SONFIN [6] FINEST [8] EFuNN [5], dmEFuNN [5] evolutionary design of neuro fuzzy systems [2] and many others [1] 3] Table 1. Comparison between neural networks and...
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BibTeX entry: (Update)
Kasabov N and Qun Song, Dynamic Evolving Fuzzy Neural Networks with 'mout -of-n' Activation Nodes for On-line Adaptive Systems, Technical Report TR99/04, Department of information science, University of Otago, 1999. http://citeseer.ist.psu.edu/kasabov99dynamic.html More
@misc{ kasabov99dynamic,
author = "N. Kasabov",
title = "Dynamic Evolving Fuzzy Neural Networks with 'mout -of-n' Activation Nodes
for On-line Adaptive Systems",
text = "Kasabov N and Qun Song, Dynamic Evolving Fuzzy Neural Networks with 'mout
-of-n' Activation Nodes for On-line Adaptive Systems, Technical Report TR99/04,
Department of information science, University of Otago, 1999.",
year = "1999",
url = "citeseer.ist.psu.edu/kasabov99dynamic.html" }
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