没有合适的资源?快使用搜索试试~ 我知道了~
首页通信状态引导的时延神经网络分布式估计算法设计
通信状态引导的时延神经网络分布式估计算法设计
0 下载量 30 浏览量
更新于2024-08-14
收藏 1.64MB PDF 举报
本文主要探讨了在存在分布式延迟和不完美测量情况下,如何设计远程估计器来优化时延神经网络(Time-Delay Neural Networks, TDNN)的性能。研究焦点在于处理通信过程中的不确定性,特别是由神经元引起的随机非线性测量误差。这些测量结果通过多条传输通道进行传输,为了反映实际通信中的波动性,文中引入了马尔可夫链模型来模拟数据包丢失现象。 马尔可夫链被用来刻画每个通道的数据包掉包情况,其状态可以用来表示通信质量的瞬息变化。作者提出了一种将独立的马尔可夫链转化为增强模型的方法,这有助于系统层面的分析。他们引入了一个新的变量,称为“通道状态”,这个状态变量考虑了数据包丢包的情况,这对于准确评估和预测网络性能至关重要。 在设计过程中,作者考虑到如何在保持估计器数量的同时,平衡估计精度。他们开发了一种基于通道状态的估计器,这种估计器能够动态调整以适应不同的通信条件,从而在面临网络延迟和噪声挑战时,有效地提高了远程估计的稳定性和有效性。此外,由于是随机神经元依赖的非线性模型,该研究还可能涉及神经网络的训练策略和适应性,确保在复杂通信环境中能自适应地学习和优化。 这篇研究论文深入探讨了通信状态信息在设计远程时延神经网络估计器中的关键作用,为解决分布式系统中的实时估计问题提供了一种创新的解决方案,对于通信、信号处理和神经网络优化等领域具有重要的理论和实践价值。
资源详情
资源推荐
![](https://csdnimg.cn/release/download_crawler_static/18085369/bg1.jpg)
This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 1
Remote Estimator Design for Time-Delay Neural
Networks Using Communication State Information
Yong Xu, Member, IEEE, Chang Liu, Renquan Lu , Senior Member, IEEE,and
Chun-Yi Su, Senior Member, IEEE
Abstract—This paper investigates the estimator design for the
neural networks, where distributed delays and imperfect mea-
surements are included. A randomly occurred neuron-dependent
nonlinearity is used to describe the uncertain measurements
disturbed by neurons. The measurements are transmitted over
multiple transmission channels, and Markov chains are intro-
duced to model packet dropouts of these channels. A one-to-
one map is constructed to transform m independent Markov
chains to an augmented one to facilitate system analysis. A new
variable called channel state is defined based on the cases of
packet dropouts, and the channel-state-dependent estimator is
designed to trade off between the number and the performance
of the estimator. Sufficient conditions are established to guarantee
that the augmented system is stochastically stable and satisfies the
strict ( Q, S, R)− γ −dissipativity. The estimator gains are derived
using linear matrix methods. Finally, an example is applied to
illustrate the effectiveness of the developed methods.
Index Terms— Dissipative estimator, distributed delays,
Markov chain, neural networks, packet dropouts.
I. INTRODUCTION
N
EURAL networks (NNs) have attracted intensive atten-
tion in the past several decades, since they are widely
applied in the fields of nonlinear system analysis and opti-
mization, industrial processes, channel equalization, and so
on [1]–[7]. Time-delay systems widely exist in our daily
life, and a number of researchers have paid attention to the
NNs with time delays to further extend the application of the
NNs [8]. In [9], the exponential mean-square stability was
analyzed for the stochastic NNs with state time delays. In [10],
the passive performance of the Markov jump NNs was studied
with time-varying delays and distributed delays. What is more,
Manuscript received July 18, 2017; revised October 29, 2017; accepted
January 3, 2018. This work was supported in part by the National Natural
Science Foundation of China under Grant 61503106 and Grant U1611262,
in part by the China National Funds for Distinguished Young Scientists under
Grant 61425009, and in part by the Fundamental Research Funds for the
Central Universities under Grant 2017FZA5010. The work of R. Lu was sup-
ported by the Guangdong Province Higher Vocational Colleges and Schools
Pearl River Scholar approved in 2015. The work of Y. Xu was supported by
the Guangdong Province Higher Vocational Colleges and Schools Pearl River
Young Scholar approved in 2017. (Corresponding author: Renquan Lu.)
Y. Xu, C. Liu, and R. Lu are with the Guangdong Key Laboratory of
IoT Information Technology, School of Automation, Guangdong University
of Technology, Guangzhou 510006, China (e-mail: xuyong809@163.com;
liuchangwm@163.com; rqlu@gdut.edu.cn).
C.-Y. Su is with the Guangdong Key Laboratory of IoT Information
Technology, School of Automation, Guangdong University of Technology,
Guangzhou 510006, China, on leave from the Department of Mechanical
Engineering, Concordia University, Montreal, H3G 1M8, Canada.
Color versions of one or more of the figures in this paper are available
online at http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/TNNLS.2018.2793185
the NNs have been used to analyze the stability and design the
controller for nonlinear systems with time delays [11]. In [12],
the adaptive NNs were employed to handle the consensus
problem of the nonlinear multiagent sytems with time delays.
Thus, in order to further understand the properties of the
biological NNs and extend the application of the artificial NNs
in the smart control fields, studying on the NNs with time
delays is still a challenging problem.
The internal states of the NNs are complex (i.e., they possess
the properties of dynamics and nonlinearity), and always
cannot be measured directly. Thus, designing an estimator
based on the available measurements is requisite for the NNs.
What we have to point out is that the measurements are
always imperfect, bringing a lot of difficulties to design the
estimator, are mainly caused by two factors [13]. One is that
sensors are unavoidably disturbed by external noises, such
as Gaussian noises and energy bounded noises [14], [15].
The other is that measurements are influenced by the system
dynamics, such as the measurements of the NNs are disturbed
by the neurons [16]. Considering the second factor, a robust
filter was designed using the measurements with neuron-
dependent nonlinearities for the NNs with time delays [17].
This paper pays attention to randomly occurred neuron-
dependent nonlinearities, which are more general than the
existing ones, and how to use such measurements to design
an estimator deserves more attention.
In order to further promote the development of the NNs,
the obtained measurements are always transmitted over the
networks to realize remote state estimation. Thus, networked
systems possessing numerous advantages have been intro-
duced to the NNs. For the large scalar networked sys-
tems, the following facts make that transmission over the
multiple channels become an unavoidable issue [18], [19].
First, the capacity of the transmission channels is always
limited, being unavailable to transmit all of the information in
one packet [20]. Second, sensors are distributed in a large area,
it is unable to collect all sensor measurements and transmit
them together [21]. In [22], the linear quadratic controller was
designed for the multiple transmission channels with fading
property, and a sufficient and necessary condition for the
existence of the optimal controller was established. In [23],
the state and the output feedback controllers were designed
for the mutually dependent multiple transmission channels.
One drawback of the shared communication channels of the
networked control systems is packet dropouts, and there are
two reasons, which cause packet dropouts [24]–[28]. The first
one is that the shared communication channel is influenced
2162-237X © 2018 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
下载后可阅读完整内容,剩余9页未读,立即下载
![pdf](https://img-home.csdnimg.cn/images/20210720083512.png)
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://profile-avatar.csdnimg.cn/default.jpg!1)
weixin_38641339
- 粉丝: 12
- 资源: 927
上传资源 快速赚钱
我的内容管理 展开
我的资源 快来上传第一个资源
我的收益
登录查看自己的收益我的积分 登录查看自己的积分
我的C币 登录后查看C币余额
我的收藏
我的下载
下载帮助
![](https://csdnimg.cn/release/wenkucmsfe/public/img/voice.245cc511.png)
最新资源
- 解决Eclipse配置与导入Java工程常见问题
- 真空发生器:工作原理与抽吸性能分析
- 爱立信RBS6201开站流程详解
- 电脑开机声音解析:故障诊断指南
- JAVA实现贪吃蛇游戏
- 模糊神经网络实现与自学习能力探索
- PID型模糊神经网络控制器设计与学习算法
- 模糊神经网络在自适应PID控制器中的应用
- C++实现的学生成绩管理系统设计
- 802.1D STP 实现与优化:二层交换机中的生成树协议
- 解决Windows无法完成SD卡格式化的九种方法
- 软件测试方法:Beta与Alpha测试详解
- 软件测试周期详解:从需求分析到维护测试
- CMMI模型详解:软件企业能力提升的关键
- 移动Web开发框架选择:jQueryMobile、jQTouch、SenchaTouch对比
- Java程序设计试题与复习指南
资源上传下载、课程学习等过程中有任何疑问或建议,欢迎提出宝贵意见哦~我们会及时处理!
点击此处反馈
![](https://img-home.csdnimg.cn/images/20220527035711.png)
![](https://img-home.csdnimg.cn/images/20220527035711.png)
![](https://img-home.csdnimg.cn/images/20220527035111.png)
安全验证
文档复制为VIP权益,开通VIP直接复制
![](https://csdnimg.cn/release/wenkucmsfe/public/img/green-success.6a4acb44.png)