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This brief focuses on reachable set estimation for memristive complex-valued neural networks (MCVNNs) with disturbances. Based on algebraic calculation and Gronwall-Bellman inequality, the states of MCVNNs with bounded input disturbances… Click to show full abstract
This brief focuses on reachable set estimation for memristive complex-valued neural networks (MCVNNs) with disturbances. Based on algebraic calculation and Gronwall-Bellman inequality, the states of MCVNNs with bounded input disturbances converge within a sphere. From this, the convergence speed is also obtained. In addition, an observer for MCVNNs is designed. Two illustrative simulations are also given to show the effectiveness of the obtained conclusions.
Keywords:
reachable set;
estimation memristive;
neural networks;
set estimation;
complex valued;
memristive complex
Journal Title: IEEE transactions on neural networks and learning systems
Year Published: 2022
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Journal Title: IEEE transactions on neural networks and learning systems
Year Published: 2022
Link to full text (if available)
Share on Social Media: Sign Up to like & get
recommendations!