Conic Section Function Neural Network Circuitry for Offline Signature Recognition

Erkmen B., Kahraman N., Vural R., Yıldırım T.

IEEE TRANSACTIONS ON NEURAL NETWORKS, vol.21, no.4, pp.667-672, 2010 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 21 Issue: 4
  • Publication Date: 2010
  • Doi Number: 10.1109/tnn.2010.2040751
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.667-672
  • Keywords: Chip-in-the-loop learning technique, conic section function neural networks, mixed-mode integrated circuit design, offline signature recognition, VERIFICATION
  • Yıldız Technical University Affiliated: Yes


In this brief, conic section function neural network (CSFNN) circuitry was designed for offline signature recognition. CSFNN is a unified framework for multilayer perceptron (MLP) and radial basis function (RBF) networks to make simultaneous use of advantages of both. The CSFNN circuitry architecture was developed using a mixed mode circuit implementation. The designed circuit system is problem independent. Hence, the general purpose neural network circuit system could be applied to various pattern recognition problems with different network sizes on condition with the maximum network size of 16-16-8. In this brief, CSFNN circuitry system has been applied to two different signature recognition problems. CSFNN circuitry was trained with chip-in-the-loop learning technique in order to compensate typical analog process variations. CSFNN hardware achieved highly comparable computational performances with CSFNN software for nonlinear signature recognition problems.