Erkmen Burcu, Kahraman Nihan, Vural Revna A, Yildirim Tulay
Electronics and Communication Engineering, Yildiz Technical University, Istanbul, Turkey.
IEEE Trans Neural Netw. 2010 Apr;21(4):667-72. doi: 10.1109/TNN.2010.2040751. Epub 2010 Feb 8.
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.
在本简报中，设计了圆锥曲线函数神经网络（CSFNN）电路用于离线签名识别。CSFNN是多层感知器（MLP）和径向基函数（RBF）网络的统一框架，能同时利用两者的优势。CSFNN电路架构是使用混合模式电路实现开发的。所设计的电路系统与问题无关。因此，通用神经网络电路系统在最大网络规模为16 - 16 - 8的条件下，可应用于不同网络规模的各种模式识别问题。在本简报中，CSFNN电路系统已应用于两个不同的签名识别问题。CSFNN电路采用片上学习技术进行训练，以补偿典型的模拟工艺变化。对于非线性签名识别问题，CSFNN硬件实现了与CSFNN软件高度可比的计算性能。
