Jiejie Chen

Global Mittag-Leffler stability and synchronization of memristor-based fractional-order neural networks

The present paper introduces memristor-based fractional-order neural networks. The conditions on the global Mittag-Leffler stability and synchronization are established by using Lyapunov method for these networks. The analysis in the paper employs results from the theory of fractional-order differential equations with discontinuous right-hand sides. The obtained results extend and improve some previous works on conventional memristor-based recurrent neural networks.

On the periodic dynamics of memristor-based neural networks with time-varying delays

Abstract In this paper, we study the existence, uniqueness and stability of periodic solution for a wide class of memristor-based neural networks with time-varying delays. By employing the topological degree theory in set-valued analysis, differential inclusions theory and a new Lyapunov function method, we prove that the neural network has a unique periodic solution, which is globally exponentially stable. Moreover, we prove the existence, uniqueness and global exponential stability of equilibrium point for time-varying delayed memristor-based neural networks with constant coefficients. The obtained results improve and extend previous works on memristor-based or usual neural network dynamical systems with continuous or discontinuous right-hand side. Finally, two numerical examples are provided to show the applicability and effectiveness of our main results.

Global exponential almost periodicity of a delayed memristor-based neural networks

In this paper, the existence, uniqueness and stability of almost periodic solution for a class of delayed memristor-based neural networks are studied. By using a new Lyapunov function method, the neural network that has a unique almost periodic solution, which is globally exponentially stable is proved. Moreover, the obtained conclusion on the almost periodic solution is applied to prove the existence and stability of periodic solution (or equilibrium point) for delayed memristor-based neural networks with periodic coefficients (or constant coefficients). The obtained results are helpful to design the global exponential stability of almost periodic oscillatory memristor-based neural networks. Three numerical examples and simulations are also given to show the feasibility of our results.

Deformation prediction of landslide based on functional network

This paper proposes functional networks as novel intelligence paradigm scheme for landslide displacement prediction. They evaluate unknown neuron functions from given functional families during the training process. General functional networks with two variables training data set (GFN), separable functional networks (SFN) and associativity functional networks (AFN) are applied to forecast a real-world example. In addition, we compare them with back-propagation neural network (BPNN) in terms of the same measurements. The results reveal that the landslide displacement prediction using functional networks is reasonable and effective, and GFN are consistently better than the other two types of functional networks and BPNN.

Almost periodic solutions for a memristor-based neural networks with leakage, time-varying and distributed delays

In this paper, we study the existence and global exponential stability of almost periodic solution for memristor-based neural networks with leakage, time-varying and distributed delays. Using a new Lyapunov function method, we prove that this delayed neural network has a unique almost periodic solution, which is globally exponentially stable. Moreover, the obtained conclusion on the almost periodic solution is applied to prove the existence and stability of periodic solution (or equilibrium point) for this delayed neural network with periodic coefficients (or constant coefficients).

On the periodic dynamics of memristor-based neural networks with leakage and time-varying delays

In this paper, the periodic dynamics have been studied for a general kind of memristor-based neural networks with leakage and time-varying delays. Some new sufficient conditions have been derived ensuring that the existence, uniqueness and globally exponential stability of the periodic solution for the neural network by using differential inclusions theory, the topological degree theory in set-valued analysis and Lyapunov function technique and so on. As a special case, we have shown that the existence, uniqueness and global exponential stability of equilibrium point for the autonomous neural networks with leakage delays.

Analysis and design of winner-take-all behavior based on a novel memristive neural network

In this paper, some sufficient conditions are derived to guarantee a novel memristive neural network for realizing winner-take-all behavior. Some design methods for synthesizing the winner-take-all behavior based on the memristive neural network are developed by using the obtained results. Finally, simulation results demonstrate the validity and characteristics of the proposed approach.

Generalized Regression Neural Networks with K-Fold Cross-Validation for Displacement of Landslide Forecasting

This paper proposes a generalized regression neural networks (GRNNS) with \(K\)-fold cross-validation (GRNNSK) for predicting the displacement of landslide. Furthermore, correlation analysis is a fundamental analysis to find the potential input variables for a forecast model. Pearson cross-correlation coefficients (PCC) and mutual information (MI) are applied in the paper. Test on the case study of Liangshuijing (LSJ) landslide in the Three Gorges reservoir in China demonstrate the effectiveness of the proposed approach.

Global uniform asymptotic fixed deviation stability and stability for delayed fractional-order memristive neural networks with generic memductance

In this paper, we study global uniform asymptotic fixed deviation stability and stability for a wide class of memristive neural networks with time-varying delays. Firstly, a new mathematical expression of the generic memductance (memristance) is proposed according to the feature of the memristor and the general current-voltage characteristic and a new class of neural networks is designed. Next, a new concept of stability (fixed deviation stability) is proposed in order to describe veritably the stability characteristics of the discontinuous system and the sufficient conditions are given to guarantee the global uniform asymptotic fixed deviation stability and stability of the new system. Finally, two numerical examples are provided to show the applicability and effectiveness of our main results.

Application of multi-gene genetic programming based on separable functional network for landslide displacement prediction

Complexity of analysis of landslide hazard is due to uncertainty. In this study, a novel approach multi-gene genetic programming based on separable functional network (MGGPSFN) is presented for predicting landslide displacement. Moreover, Pearsons cross-correlation coefficients and mutual information are adopted to look for the potential input variables for a forecast model in the paper. The performance of new model is verified through one case study in Baishuihe landslide in the Three Gorges Reservoir in China. In addition, we compared it with two methods, back-propagation neural network and radial basis function, and MGGPSFN got the best results in the same measurements.