Neural networks

In this paper, a class of recurrent neural networks with discrete and continuously distributed delays is considered. Sufficient conditions for the existence, uniqueness, and global exponential stability of a periodic solution are obtained by using contraction mapping theorem and stability theory on impulsive functional differential equations. The proposed method, which differs from the existing results in the literature, shows that network models may admit a periodic solution which is globally exponentially stable via proper impulsive control strategies even if it is originally unstable or divergent. Two numerical examples and their computer simulations are offered to show the effectiveness of our new results.

Impulsive Control for Existence, Uniqueness, and Global Stability of Periodic Solutions of Recurrent Neural Networks With Discrete and Continuously Distributed Delays

Sentiment analysis (SA), also called Opinion Mining (OM) is the task of extracting and analyzing people’s opinions, sentiments, attitudes, perceptions, etc., toward different entities such as topics, products, and services. The fast evolution of Internet-based applications like websites, social networks, and blogs, leads people to generate enormous heaps of opinions and reviews about products, services, and day-to-day activities. Sentiment analysis poses as a powerful tool for businesses, governments, and researchers to extract and analyze public mood and views, gain business insight, and make better decisions. This paper presents a complete study of sentiment analysis approaches, challenges, and trends, to give researchers a global survey on sentiment analysis and its related fields. The paper presents the applications of sentiment analysis and describes the generic process of this task. Then, it reviews, compares, and investigates the used approaches to have an exhaustive view of their advantages and drawbacks. The challenges of sentiment analysis are discussed next to clarify future directions.

A comprehensive survey on sentiment analysis: Approaches, challenges and trends

Global stability of complex-valued neural networks with both leakage time delay and discrete time delay on time scales

The problem of stochastic robust stability of a class of stochastic Hopfield neural networks with time-varying delays and parameter uncertainties is investigated in this paper. The parameter uncertainties are time-varying and norm-bounded. The time-delay factors are unknown and time-varying with known bounds. Based on Lyapunov–Krasovskii functional and stochastic analysis approaches, some new stability criteria are presented in terms of linear matrix inequalities (LMIs) to guarantee the delayed neural network to be robustly stochastically asymptotically stable in the mean square for all admissible uncertainties. Numerical examples are given to illustrate the effectiveness and less conservativeness of the developed techniques.

Novel robust stability criteria for uncertain stochastic Hopfield neural networks with time-varying delays

Firstly, we propose a concept of uniformly almost periodic functions on almost periodic time scales and investigate some basic properties of them. When time scale or , our definition of the uniformly almost periodic functions is equivalent to the classical definitions of uniformly almost periodic functions and the uniformly almost periodic sequences, respectively. Then, based on these, we study the existence and uniqueness of almost periodic solutions and derive some fundamental conditions of admitting an exponential dichotomy to linear dynamic equations. Finally, as an application of our results, we study the existence of almost periodic solutions for an almost periodic nonlinear dynamic equations on time scales.

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Yongkun Li

Papers

Uniformly Almost Periodic Functions and Almost Periodic Solutions to Dynamic Equations on Time Scales

Stability and existence of periodic solutions to delayed Cohen-Grossberg BAM neural networks with impulses on time scales

Existence and globally exponential stability of almost periodic solution for Cohen–Grossberg BAM neural networks with variable coefficients

Anti-periodic solutions to impulsive shunting inhibitory cellular neural networks with distributed delays on time scales

Anti-periodic solutions for Cohen-Grossberg neural networks with bounded and unbounded delays