In this paper, we study the distributed synchronization and pinning distributed synchronization of stochastic coupled neural networks via randomly occurring control. Two Bernoulli stochastic variables are used to describe the occurrences of distributed adaptive control and updating law according to certain probabilities. Both distributed adaptive control and updating law for each vertex in a network depend on state information on each vertex's neighborhood. By constructing appropriate Lyapunov functions and employing stochastic analysis techniques, we prove that the distributed synchronization and the distributed pinning synchronization of stochastic complex networks can be achieved in mean square. Additionally, randomly occurring distributed control is compared with periodically intermittent control. It is revealed that, although randomly occurring control is an intermediate method among the three types of control in terms of control costs and convergence rates, it has fewer restrictions to implement and can be more easily applied in practice than periodically intermittent control.
This paper presents a systematic review on the state-of-art of artificial intelligence (AI) applications in the apparel industry. The existing literature is reviewed based on different research issues and AI-based methodologies. The research issues are categorized into four categories on the basis of the operation processes of the apparel industry, including apparel design, manufacturing, retailing, and supply chain management. This paper shows that research on AI applications in the apparel industry is still limited by analyzing the limitations of previous studies and research challenges. Finally, suggestions for further studies are offered.
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