Supervised and unsupervised fuzzy–adaptive Hamming net

✍ Scribed by Paul S. Wu; Ming Li

• Publisher: Elsevier Science
• Year: 1999
• Tongue: English
• Weight: 288 KB
• Volume: 32
• Category: Article
• ISSN: 0031-3203
• DOI: 10.1016/s0031-3203(98)00049-1

✦ Synopsis

A new fuzzy}adaptive Hamming net with supervised and unsupervised learning is proposed in this paper. The new neural model is derived from fuzzy}adaptive Hamming net and retains the advantage of deleting the search time that is a potential serious problem for ART model. The new neural model sets di!erent vigilance parameters for di!erent clusters (hyper-rectangles will be used in this paper). This feature makes input pattern classi"cation more e$cient when di!erent hyper-rectangles occupy di!erent sizes of characteristic spaces. The newly proposed supervised and unsupervised learning rule which only establishes non-cross overlapped hyper-rectangles, does not a!ect other hyper-rectangles during expansion and makes the classi"cation more stable. In addition, the new learning rule allows the creation of nested hyper-rectangles in order to resolve the problem of non-convex input patterns. Simulations of the new net to palm prints recognition have been performed and good performance has been demonstrated.