Fixed-Time Synchronization of Delayed Memristive Neural Networks with Discontinuous Activations | Zendy

Hao Pu | Zendy; Fengjun Li | Zendy

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Fixed-Time Synchronization of Delayed Memristive Neural Networks with Discontinuous Activations

Author(s) -

Hao Pu,

Fengjun Li

Publication year - 2021

Publication title -

journal of mathematics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.252

H-Index - 13

eISSN - 2314-4785

pISSN - 2314-4629

DOI - 10.1155/2021/3350534

Subject(s) - differential inclusion , synchronization (alternating current) , mathematics , control theory (sociology) , correctness , memristor , artificial neural network , controller (irrigation) , function (biology) , simple (philosophy) , stability (learning theory) , constant (computer programming) , computer science , topology (electrical circuits) , control (management) , mathematical optimization , algorithm , artificial intelligence , agronomy , philosophy , epistemology , combinatorics , evolutionary biology , machine learning , electrical engineering , biology , programming language , engineering

In this paper, the fixed-time synchronization problem for a class of memristive neural networks with discontinuous neuron activation functions and mixed time-varying delays is investigated. With the help of the fixed-time stability theory, under the framework of Filippov solution and differential inclusion theory, several new and useful sufficient criteria for fixed-time synchronization are obtained by designing two types of energy-saving and simple controllers for the considered systems. Compared with the traditional fixed-time synchronization controller, the controllers used in this paper only have one power exponent term, which is a function of the system state error rather than a constant. Moreover, some previous relevant works are especially improved. Finally, two numerical examples are given to show the correctness and the effectiveness of the obtained theoretical results.

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