Cellular neural network with trapezoidal activation function

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Atıf İçin Kopyala

Bilgili E., Goknar I., Ucan O.

INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS, cilt.33, sa.5, ss.393-417, 2005 (SCI-Expanded)

• Yayın Türü: Makale / Tam Makale
• Cilt numarası: 33 Sayı: 5
• Basım Tarihi: 2005
• Doi Numarası: 10.1002/cta.328
• Dergi Adı: INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS
• Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
• Sayfa Sayıları: ss.393-417
• İstanbul Üniversitesi Adresli: Hayır

Özet

This paper presents a cellular neural network (CNN) scheme employing a new non-linear activation function, called trapezoidal activation function (TAF). The new CNN structure can classify linearly non-separable data points and realize Boolean operations (including eXclusive OR) by using only a single-layer CNN. In order to simplify the stability analysis, a feedback matrix W is defined as a function of the feedback template A and 2D equations are converted to 1D equations. The stability conditions of CNN with TAF are investigated and a sufficient condition for the existence of a unique equilibrium and global asymptotic stability is derived. By processing several examples of synthetic images, the analytically derived stability condition is also confirmed. Copyright (c) 2005 John Wiley & Sons, Ltd.