Artificial neural network (NN) is often called NN for short. The dynamics of NNs have been applied to multiple fields, such as associative memory, autonomous navigation, data encryption, intelligent control, medicine diagnosis, optimization, pattern recognition, and so forth. This book provides a direct method based on system solutions to address the problems related to the analysis and control of Delayed Neural Networks. The method proposed in this book is important for the following reasons: It does not involve the construct of any Lyapunov-Krasovskii functional (LKF), which overcomes the difficulty in constructing an appropriate and effective LKF; It can provide more simpler sufficient conditions, and hence it possesses less computational complexity; It can result in delay-dependent global exponential stability criteria that can used to give the decay rate estimation of the state; It is suitable for analysis and design problems of most system models with (multiple) delays after a small modification. The book is divided into 11 chapters, and focuses on the analysis and design problems related to Delayed Neural Networks. It is written for graduate students and research level mathematicians and is suitable for postgraduates or as a reference.

Analysis and Design of Delayed Neural Networks PDF

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