Universal Perceptron and DNA-Like Learning Algorithm for Binary Neural Networks: LSBF and PBF Implementations

Universal perceptron (UP), a generalization of Rosenblatt's perceptron, is considered in this paper, which is capable of implementing all Boolean functions (BFs). In the classification of BFs, there are: 1) linearly separable Boolean function (LSBF) class, 2) parity Boolean function (PBF) class...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems 2009-10, Vol.20 (10), p.1645-1658
Hauptverfasser:	Chen, Fangyue, Chen, Guanrong Ron, He, Guolong, Xu, Xiubin, He, Qinbin
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Applied sciences Artificial intelligence Backpropagation algorithms Biological systems Biomimetics - methods Boolean functions Cellular neural network (CNN) Cellular neural networks Computer science control theory systems Computers, Molecular Connectionism. Neural networks Convergence Deoxyribonucleic acid DNA DNA - chemistry DNA - genetics DNA-like learning algorithm Exact sciences and technology linearly separable Boolean function (LSBF) multilayer perceptron (MLP) Neural networks Neural Networks (Computer) Neurons nonlinearly separable degree (NLSD) parity Boolean function (PBF) Pattern Recognition, Automated - methods Robustness Sequences Signal Processing, Computer-Assisted single-layer perceptron (SLP) Studies universal perceptron (UP)
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