Special Issue on Neural-Network-based Optimization and Analysis for Nonlinear Stochastic Systems
• 大类 : 工程技术 - 2区
• 小类 : 计算机：人工智能 - 2区
Neural networks are widely used learning machines with strong learning ability and adaptability, which have been extensively applied in intelligent control field on parameter optimization, anti-disturbance of random factors, etc., and neural network- based stochastic optimization and control have applications in a broad range of areas. As one of the most important control models, stochastic systems widely exist in real world, such as mobile sensor networks, multi-agent systems, unmanned aerial vehicles and aircrafts, etc. However, due to the influence of random factors, there are still many challenging issues arising from nonlinear stochastic systems. In particular, some challenges existing in the implementation and controller design which are related to neural networks, intelligent data sensing, secure information processing due to network resource constraints. As a result, it is indispensable to understand how to reliably, resiliently and safely apply neural networking technology to nonlinear stochastic systems with a large number of distributed sensors, controllers and actuators, which renders some fundamental problems regarding real-time and intelligent information processing.
The main focus of this special issue is to provide an opportunity for researchers and engineers to present their latest results in nonlinear stochastic optimization and controller design based on neural networks. We thus welcome both theoretical work and application-oriented studies. All submitted papers will be peer-reviewed and selected based on both their quality and relevance.
The list of possible topics includes, but is not limited to: