This paper is concerned with dissipativity analysis of complex-valued bidirectional associative memory (BAM) neural networks (NNs) with time delay. Some novel sufficient conditions that guarantee the dissipativity of complex-valued BAM neural networks (CVBNNs) are obtained by using the inequality techniques, Halanay inequality, and upper right Dini derivative concepts. The complex-valued nonlinear function is separated into its real and imaginary parts to a set of sufficient conditions for the global dissipativity of CVBNNs by using the matrix measure method. Moreover, the global attractive sets are obtained, which are positive invariant sets. Finally, numerical simulations are provided to demonstrate the effectiveness of the proposed theoretical results.
- Complex-valued BAM neural networks
- Matrix measure
ASJC Scopus subject areas
- Artificial Intelligence