A novel result on stability analysis for uncertain neutral stochastic time-varying delay systems

Deng, Feiqi; Mao, Weihua; Wan, Anhua

doi:10.1016/j.amc.2013.05.071

Cited by 36 publications

(33 citation statements)

References 34 publications

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“…Neural information may transfer across chemical reactivity during the biochemistry experiments, which leads to a neutral-type process [7,9]. Most recently, the problem of robust stability of neutral stochastic system has been considered in [5,10,23]. As for the study of neutral neural networks, some available works for the stability and filtering of neutral neural networks have been presented in [7,19,38].…”

Section: Introductionmentioning

confidence: 99%

Robust stability and $$H_{\infty}$$ H ∞ filter design for neutral stochastic neural networks with parameter uncertainties and time-varying delay

Hua

Tan

Fei

et al. 2015

Int. J. Mach. Learn. & Cyber.

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This paper is concerned with the problem of robust stability and H 1 filter design for neutral stochastic neural networks with parameter uncertainties and timevarying delay. The parameter uncertainties are assumed to be norm-bounded. With the Lyapunov-krasovskii theory, several delay-dependent stability conditions are obtained in terms of liner matrix inequalities(LMIs). Based on the obtained stability criteria, some sufficient conditions for the existence of the robust H 1 filter are derived. The obtained results ensure the robust stability and a prescribed H 1 performance level of the filtering error systems for all admissible uncertainties. Finally, two numerical examples are given. One is provided to demonstrate the stability analysis results in this paper are less conservative than some existing results. The other is provided to illustrate the effectiveness of the filter design method.

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Section: Introductionmentioning

confidence: 99%

Robust stability and $$H_{\infty}$$ H ∞ filter design for neutral stochastic neural networks with parameter uncertainties and time-varying delay

Hua

Tan

Fei

et al. 2015

Int. J. Mach. Learn. & Cyber.

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Section: Robust Control and Stability Analysis Of Stochastic Systemsmentioning

confidence: 99%

“…For example, [5][6] studied the stability for stochastic delay systems and random Markov delay systems by introducing free weighting matrices, and obtained corresponding delay-dependent stability criteria respectively. [7] discussed the stability of neutral stochastic uncertain delay systems. [8] extended the integral inequality method to the nonlinear stochastic uncertain delay systems, and obtained a delay-dependent stability criteria.…”

Section: Robust Control and Stability Analysis Of Stochastic Systemsmentioning

confidence: 99%

“…For example, [5-6] studied the stability for stochastic delay systems and random Markov delay systems by introducing free weighting matrices, and obtained corresponding delay-dependent stability criteria respectively. [7] discussed the stability of neutral stochastic uncertain delay systems.[8] extended the integral inequality method to the nonlinear stochastic uncertain delay systems, and obtained a delay-dependent stability criteria.[9] studied the robust exponential stability of a kind of neutral stochastic Markov jump systems, and gave the sufficient conditions of robust exponential stability for the systems.[10] studied the stability of a kind of neutral stochastic neural networks with Markov jump by using the segmentation method. Due to limitations of stochastic Lyapunov stability theory, the robust control of stochastic systems has been unable to get faster development.…”

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confidence: 99%

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Robust Analysis and Control of Stochastic Interval Systems

Wang¹,

Rao²

2015

Proceedings of the 3rd International Conference on Material, Mechanical and Manufacturing Engineering

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Abstract. Stochastic interval systems is a new type of stochastic system in recent years, which is a new field integrated by information and control. This paper introduces the research history and the newest research trends of stochastic interval systems, and presents many widespread theoretical and application problems. Moreover, some new research topics and directions related to stochastic interval systems are proposed. IntroductionFor many problems in natural world, socio-economic, material systems of practical engineering, their dynamic rules can be described by deterministic dynamic model or random system model. The description of deterministic system model is by deterministic ordinary differential equations, which is to simplify the process of the actual system. Its system model is relatively simple, and is easy to analyze and synthesize, and its research tool is also relatively simple. When the considered system is under ideal conditions or system accuracy is not required, the system is often reduced to a deterministic model.Since the creation of modern control theory, control theory based on deterministic system model has been developing rapidly, and played an important role in practical applications. However, in many practical systems, such as engineering controls, traffic management, physical circuit system, the random factor is objective and inevitable. Due to the perturbations by the internal structure parameter, the system control inputs and the external environment, the established model can not accurately describe the controlled object or the system creates deviation between the running model and the actual object. In large-scale integrated circuits, interference of random factors is inevitable. To ignore the interference of random factors will increase the systematic errors and affect system performance. On the radio circuit receiving system, due to the random noise disturbance outside, the received signal of a radio is unstable. Thus, random factors are worth considering.With the development of science and technology, the system accuracy requirements of practical engineering technology become more and more high. The original simplified deterministic model can not meet the accuracy requirements of the system. The system designed by deterministic model in operation will deviate from predetermined design requirements, resulting in the generation of random deviation. Therefore, in the system modeling, we should fully consider the impact of random factors on the system, and use a stochastic model for system modeling and use the stochastic point of view for the system analysis and synthesis. Stochastic system model is described by stochastic ordinary differential equations. In a sense, the theory of stochastic systems are the combination product of deterministic systems theory and stochastic processes.In most actual operation of the control system, there are more or less uncertain factors, such as the measurement error of physical components, the impact of limited word length of memory, and outside in...

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“…And when the number of delay-partitioning increases, the matrix formulation becomes more complex and the computational burden and time-consuming grow bigger [10]. In [31], [33], the mean-square exponential stability was discussed, and delay-dependent stability criteria for neutral stochastic delay systems were derived based on the generalized Finsler lemma (GFL). Unfortunately, from the preface of [34], there are some errors in this GFL.…”

mentioning

confidence: 99%