Fixed‐time synchronization in multilayer networks with delay Cohen–Grossberg neural subnets via adaptive quantitative control

Abstract

In this paper, fixed‐time synchronization of nonlinear stochastic coupling multilayer neural networks is studied. The neural subnets in the multilayer networks are delay Cohen–Grossberg neural networks (DCGNNs). To overcome uncertain factors, we designed an adaptive delay‐dependent controller in synchronization. To describe constraints of communication and other related problems in networks, which are due to limitations for bit rates and bandwidths in communication channels, an adaptive fixed‐time control strategy is purposed by introducing quantization signal input. A theoretical framework about fixed‐time synchronization in multilayer delay Cohen–Grossberg neural networks (MDCGNNs) is established. We find that fixed settling time is related to the scale of MDCGNNs, characteristics of the designed controller parameters, and level of quantization. Finally, the effective of the theoretical framework is validated in an example.

Multiple-image encryption scheme based on a new 2D hyperchaotic map with blurred pixels

“…For a secure encryption algorithm, the correlation between a pixel and its neighboring pixel values in all directions should be close to 0. The adjacency factor is an important parameter used to measure the correlation of neighboring pixels, and its value can be given by equation (14) to equation (17) for calculation as…”

“…Many encryption algorithms designed for text data, such as the Data Encryption Standard (DES) [10] and Advanced Encryption Standard (AES) [11], are generally unsuitable for the encryption of image because of the strong correlation among its adjacent pixels. Presently researchers are studying some new image encryption schemes with use of chaotic systems [12][13][14][15][16], DNA coding [17][18][19], compressive sensing [20,21], memristors [22,23], deep learning [24], and so on. In particular, chaotic systems are extensively applied in the encryption of image due to their high sensitivity, unpredictability, and other characteristics.…”

Editorial: Advances in nonlinear systems and networks, volume II

“…Due to the complexity, unpredictability, and adaptability of nonlinear systems and networks, their applications and research face significant challenges. But with the continuous development of science and technology, the research and application of nonlinear systems and networks are also deepening in various fields, such as chaotic systems [6][7][8][9][10], chaotic circuits [11][12][13][14], nonlinear devices [15][16][17], neural networks [18][19][20][21][22][23][24], neural circuits [25][26][27][28], memristors [29][30][31], system synchronization and control [32][33][34][35][36], system optimization [37-39], and related application fields [40][41][42][43].…”

Dynamic Analysis and Field-Programmable Gate Array Implementation of a 5D Fractional-Order Memristive Hyperchaotic System with Multiple Coexisting Attractors

“…Nonlinear science is theoretically significant and promising for practical applications across various life aspects. Over the past decade, researchers have extensively applied it in fields such as image encryption [1][2][3][4][5], electronic circuits [6][7][8][9][10], chaos synchronization [11][12][13][14][15], pseudo-random number generators [16][17][18][19][20], and neural networks [21][22][23][24][25], among others. The complex structure of chaotic attractors enhances their dynamic properties.…”