Classification and Enumeration of Linearly Separable Boolean Functions Based on Optimal Separation System
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Computer Networks and Communications,General Neuroscience,Software
Link
https://link.springer.com/content/pdf/10.1007/s11063-022-10781-1.pdf
Reference21 articles.
1. Chen F, He G, Chen G (2006) Realization of Boolean functions via CNN: mathematical theory, LSBF and template design. IEEE Trans Circuits Syst I Regul Pap 53(10):2203–2213
2. Chen F, Chen G, He G, Xu X, He Q (2009) Universal perceptron and DNA-like learning algorithm for binary neural networks: LSBF and PBF implementations. IEEE Trans Neural Netw 20(10):1645–1658
3. Franco L, Subirats JL, Anthony M, Jerez JM (2006) A new constructive approach for creating all linearly separable (threshold) functions. In: The 2006 IEEE international joint conference on neural network proceedings. IEEE, pp 4791–4796
4. Gruzling N (2007) Linear separability of the vertices of an n-dimensional hypercube. Ph.D. thesis, University of Northern British Columbia
5. Muroga S, Toda I, Kondo M (1962) Majority decision functions of up to six variables. Math Comput 16(80):459–472
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