期刊
NEURAL COMPUTING & APPLICATIONS
卷 19, 期 2, 页码 187-205出版社
SPRINGER LONDON LTD
DOI: 10.1007/s00521-009-0288-5
关键词
Classification; Functional link neural networks; Chebyshev functional link neural network; Particle swarm optimization; Back-propagation learning
资金
- National Research Foundation of Korea [과C6A1607] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
Functional link neural network (FLNN) is a class of higher order neural networks (HONs) and have gained extensive popularity in recent years. FLNN have been successfully used in many applications such as system identification, channel equalization, short-term electric-load forecasting, and some of the tasks of data mining. The goals of this paper are to: (1) provide readers who are novice to this area with a basis of understanding FLNN and a comprehensive survey, while offering specialists an updated picture of the depth and breadth of the theory and applications; (2) present a new hybrid learning scheme for Chebyshev functional link neural network (CFLNN); and (3) suggest possible remedies and guidelines for practical applications in data mining. We then validate the proposed learning scheme for CFLNN in classification by an extensive simulation study. Comprehensive performance comparisons with a number of existing methods are presented.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据