期刊
AUTOMATIC CONTROL AND COMPUTER SCIENCES
卷 49, 期 5, 页码 255-260出版社
ALLERTON PRESS INC
DOI: 10.3103/S0146411615050028
关键词
neuro-fuzzy network; computational intelligence; evolving system; learning algorithm; self-learning; kernel function
The paper proposes a learning method for an evolving Radial Basis Neural Network that makes it possible in an online mode to adjust not only synaptic weights but also parameters of the radial basis functions and the network architecture. A special feature of architecture learning is that a number of neurons in the network can both increase and decrease with a sequential stream of information at the system input. The implementation of the proposed algorithms has low computational complexity. The proposed evolving neural network can process data in an online mode.
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