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论文类型：期刊论文

发表时间：2018-04-01

发表刊物：IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS

收录刊物：SCIE、EI、Scopus

卷号：29

期号：4

页面范围：1388-1394

ISSN号：2162-237X

关键字：Automata networks; conjugacy; observability; recurrent neural networks; switched systems

摘要：Automata networks are a class of fully discrete dynamical systems, which have received considerable interest in various different areas. This brief addresses the observability of automata networks and switched automata networks in a unified framework, and proposes simple necessary and sufficient conditions for observability. The results are achieved by employing methods from symbolic computation, and are suited for implementation using computer algebra systems. Several examples are presented to demonstrate the application of the results.