Abstract
By introducing the principle and characteristics of constructive neural networks (CNN) and pointing out its deficiencies, fuzzy theory is adopted to improve the covering algorithms in this paper. We build “extended area” for each type of samples, eliminate the inference of the outlier, and redefine the threshold of covering algorithms. Furthermore, “sphere neighborhood” (SN) are constructed, the membership functions of test samples are given and all of the test samples are determined accordingly. First of all, the procedure of constructive fuzzy algorithm is given, then the model of constructive fuzzy neural networks (CFNN) is built, finally, CFNN is applied to search for communications signals. Extensive experimental results demonstrate the efficiency and practicability of the proposed algorithm.
This work was supported by the Natural Science Foundation of China under Grant No.60175018, No.60135010; partially by the National Grand Fundamental Research 973 Program of China under Grant No. G1998030509.
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Wang, L., Tan, Y., Zhang, L. (2005). Constructive Fuzzy Neural Networks and Its Application. In: Wang, J., Liao, X., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427391_70
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DOI: https://doi.org/10.1007/11427391_70
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25912-1
Online ISBN: 978-3-540-32065-4
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