Conic Section Function Neural Network Circuitry for Offline Signature Recognition

• Published in: IEEE transaction on neural networks and learning systems 2010-04, Vol.21 (4), p.667-672
• Main Authors: Erkmen, B., Kahraman, N., Vural, R.A., Yildirim, T.
• Format: Article
• Language: English
• Subjects: Algorithms; Application software; Applied sciences; Artificial intelligence; Chip-in-the-loop learning technique; Circuits; Computer science; control theory; systems; Computer Simulation; conic section function neural networks; Conics; Connectionism. Neural networks; Database Management Systems - instrumentation; Electric circuits; Exact sciences and technology; Forgery; Handwriting recognition; Hardware; Hidden Markov models; High performance computing; Humans; Mathematical analysis; Mathematical models; Memory and file management (including protection and security); Memory organisation. Data processing; mixed-mode integrated circuit design; Multilayer perceptrons; Nerve Net - cytology; Nerve Net - physiology; Networks; Neural networks; Neural Networks (Computer); Neurons - physiology; Nonlinear Dynamics; offline signature recognition; Pattern recognition; Pattern Recognition, Automated - methods; Pattern recognition. Digital image processing. Computational geometry; Recognition; Signatures; Software
• ISSN: 1045-9227; 2162-237X; 1941-0093; 2162-2388
• DOI: 10.1109/TNN.2010.2040751

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.