• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Optimal Communication Network-Based Quantized Control With Packet Dropouts for a Class of Discrete-Time Neural Networks With Distributed Time Delay
【24h】

Optimal Communication Network-Based Quantized Control With Packet Dropouts for a Class of Discrete-Time Neural Networks With Distributed Time Delay

机译：一类具有分布时滞的离散神经网络的基于最优通信网络的丢包量化控制

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This paper is concerned with optimal communication network-based quantized control for a discrete-time neural network with distributed time delay. Control of the neural network (plant) is implemented via a communication network. Both quantization and communication network-induced data packet dropouts are considered simultaneously. It is assumed that the plant state signal is quantized by a logarithmic quantizer before transmission, and communication network-induced packet dropouts can be described by a Bernoulli distributed white sequence. A new approach is developed such that controller design can be reduced to the feasibility of linear matrix inequalities, and a desired optimal control gain can be derived in an explicit expression. It is worth pointing out that some new techniques based on a new sector-like expression of quantization errors, and the singular value decomposition of a matrix are developed and employed in the derivation of main results. An illustrative example is presented to show the effectiveness of the obtained results.
机译：本文涉及具有离散时间延迟的离散时间神经网络的基于最优通信网络的量化控制。神经网络（工厂）的控制是通过通讯网络实现的。同时考虑量化和通信网络引起的数据包丢失。假设工厂状态信号在传输前由对数量化器量化，并且通信网络引起的数据包丢失可以用伯努利分布的白色序列描述。开发了一种新方法，可以将控制器设计减少到线性矩阵不等式的可行性，并且可以通过显式表达式得出所需的最佳控制增益。值得指出的是，开发了一些新技术，这些技术基于新的类似于扇区的量化误差表达式以及矩阵的奇异值分解，并被用于主要结果的推导中。给出了一个说明性示例，以显示所获得结果的有效性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号