通信状态引导的时延神经网络分布式估计算法设计

研究论文

30 浏览量更新于2024-08-14 收藏 1.64MB PDF 举报

身份认证购VIP最低享 7 折!

30元优惠券

资源详情

资源推荐

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

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

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

weixin_38641339

粉丝: 12
资源: 927

通信状态引导的时延神经网络分布式估计算法设计

网络控制系统中补偿随机变化时滞的史密斯预测器和神经网络联合估计方案

电子功用-循环交换指示输出的电池控制装置

Google开源低位元率音讯编解码器Lyra 源码

神经网络怎么进行信道估计

rbf神经网络进行估计

马尔可夫神经网络状态估计 matlab 代码

BP神经网络soc估计

模糊神经网络控制器设计及其在matlab中的实现

状态空间方程 bp神经网络模型

两层的卷积神经网络文本分类器和卷积神经网络分类器有什么不同（优点及缺点）

rbf神经网络进行自适应控制,由rbf网络辨识jacobian信息

递归神经网络和循环神经网络

神经网络结合迁移学习来估计锂离子电池的soc

光纤物理信息神经网络

wcommunicationstate ethercat通信状态判断

神经网络的解码器和编码器

神经网络模型及其matlab仿真程序设计

使用bp神经网络、rbf神经网络以及pso优化的rbf神经网络进行数据的预测

图神经网络是前馈神经网络还是反馈神经网络

继续上面内容，使用matlab，设计用神经网络实现控制系统的干扰观测与估计

最新资源