Global asymptotical stability analysis for a kind of discrete-time recurrent neural network with discontinuous activation functions

Finite-/Fixed-Time Synchronization of Delayed Coupled Discontinuous Neural Networks With Unified Control Schemes

In this article, it addresses the problem of finite-/fixed-time synchronization of delayed coupled discontinuous neural networks in the unified framework. To achieve the finite-/fixed-time synchronization and precise estimations of setting time, two novel different kinds of controllers are established, in which one is switching. Then, based on the finite-/fixed-time theorem and Lyapunov function theory, some useful criteria are obtained to select suitable controllers' parameters, which can guarantee error systems converge in the finite time/fixed time with respect to coupled neural networks. Moreover, corresponding estimations of the setting time are also provided. Finally, two numerical examples are introduced to show the effectiveness of the proposed control protocols.

Coupled convolution layer for convolutional neural network

We propose a coupled convolution layer comprising multiple parallel convolutions with mutually constrained filters. Inspired by biological human vision mechanism, we constrain the convolution filters such that one set of filter weights should be geometrically rotated, mirrored, or be the negative of the other. Our analysis suggests that the coupled convolution layer is more effective for lower layer where feature maps preserve geometric properties. Experimental comparisons demonstrate that the proposed coupled convolution layer performs slightly better than the original layer while decreasing the number of parameters. We evaluate its effect compared to non-constrained convolution layer using the CIFAR-10, CIFAR-100, and PlanktonSet 1.0 datasets.