Novel delay-dependent stability analysis of Takagi-Sugeno fuzzy uncertain neural networks with time varying delays

This paper presents the stability analysis for a class of neural networks with time varying delays that are represented by the Takagi^ugeno IT-S） model. The main results given here focus on the stability criteria using a new Lyapunov functional. New relaxed conditions and new linear matrix inequality-based designs are proposed that outperform the previous results found in the literature. Numerical examples are provided to show that the achieved conditions are less conservative than the existing ones in the literature.

Delay dependent stability criteria for recurrent neural networks with time varying delays

This paper aims to present some delay-dependent global asymptotic stability criteria for recurrent neural networks with time varying delays. The obtained results have no restriction on the magnitude of derivative of time varying delay, and can be easily checked due to the form of linear matrix inequality. By comparison with some previous results, the obtained results are less conservative. A numerical example is utilized to demonstrate the effectiveness of the obtained results.

ANN-Time Varying GARCH Model for Processes with Fixed and Random Periodicity

Financial Time Series Forecasting is an important tool to support both individual and organizational decisions. Periodic phenomena are very popular in econometrics. Many models have been built aiding capture of these periodic trends as a way of enhancing forecasting of future events as well as guiding business and social activities. The nature of real-world systems is characterized by many uncertain fluctuations which makes prediction difficult. In situations when randomness is mixed with periodicity, prediction is even much harder. We therefore constructed an ANN Time Varying Garch model with both linear and non-linear attributes and specific for processes with fixed and random periodicity. To eliminate the need for time series linear component filtering, we incorporated the use of Artificial Neural Networks (ANN) and constructed Time Varying GARCH model on its disturbances. We developed the estimation procedure of the ANN time varying GARCH model parameters using non parametric techniques.

LMS-LIKE ESTIMATION FOR TIME VARYING PARAMETERS

An LMS-like algorithm is applied for estimating the time-varying parameter theta-n in the linear model y(n) = phi-n-tau-theta-n + upsilon-n, which is general in the sense that none of the probabilistic properties such as stationarity, Markov property, independence and ergodicity is imposed on any of the processes {y(n)}, {phi-n}, {theta-n} and {upsilon-n}. It is shown that the alpha-th moment of the estimation error is of order of the alpha-th moment of the observation noise and the parameter variation w(n) change in equivalence theta-n - theta-n-1.

Stabilizing controller design for nonlinear fractional order systems with time varying delays

To deal with stabilizing of nonlinear affine fractional order systems subject to time varying delays,two methods for finding an appropriate pseudo state feedback controller are discussed.In the first method,using the Mittag-Lefler function,Laplace transform and Gronwall inequality,a linear stabilizing controller is derived,which uses the fractional order of the delayed system and the upper bound of system nonlinear functions.In the second method,at first a sufficient stability condition for the delayed system is given in the form of a simple linear matrix inequality(LMI)which can easily be solved.Then,on the basis of this result,a stabilizing pseudo-state feedback controller is designed in which the controller gain matrix is easily computed by solving an LMI in terms of delay bounds.Simulation results show the effectiveness of the proposed methods.

ANN-Time Varying GARCH Model: Simulations and Application in Modelling Temperature for Weather Derivatives

In economics and finance, minimising errors while building an abstract representation of financial assets plays a critical role due to its application in areas such as risk management, decision making and option pricing. Despite the many methods developed to handle this problem, modelling processes with fixed and random periodicity still remains a major challenge. Such methods include Artificial Neural networks (ANN), Fuzzy Inference system (FIS), GARCH models and their hybrids. This study seeks to extend literature of hybrid ANN-Time Varying GARCH model through simulations and application in modelling weather derivatives. The study models daily temperature of Kenya using ANN-Time Varying GARCH (1, 1), Time Lagged Feedforward neural network (TLNN) and periodic GARCH family models. Mean square error (MSE) and coefficient of determination R2 were used to determine performance of the models under study. Results obtained show that the ANN-Time Varying GARCH model gives the best results.

Parametric modeling of hypersonic ballistic data based on time varying auto-regressive model

For describing target motion in hypersonic vehicle defense,a parametric analyzing and modeling method on ballistic data is proposed based on time varying auto-regressive method.Ballistic data are regarded as non-stationary random signal,where the hidden internal law is studied.Firstly,ballistic data are decomposed into smooth linear trend signal and non-stationary periodic skip signal with ensemble empirical mode decomposition method to avoid mutual interference between different modal data.Secondly,the linear trend signal and the periodic skip signal are modeled separately.The linear trend signal is approximated by power function regressive estimator and the periodic skip signal is modeled based on time varying auto-regressive method.In order to determine optimal model orders,a novel method is presented based on information theoretic criteria and the criteria of minimizing the mean absolute error.Finally,the consistency test is conducted by investigating the time-frequency spectrum characteristics and statistical properties of outputs of the parametric model established above and dynamics model under the same initial condition.Simulation results demonstrate that the parametric model established by the proposed method shares a high consistency with the original dynamics model.

Control Schemes for Passive Teleoperation Systems over Wide Area Communication Networks with Time Varying Delay

In this paper,the problem of time varying telecommunication delays in passive teleoperation systems is addressed.The design comprises delayed position,velocity and position-velocity signals with the local position and velocity signals of the master and slave manipulators.Nonlinear adaptive control terms are employed locally to cope with uncertain parameters associated with the gravity loading vector of the master and slave manipulators.Lyapunov-Krasovskii function is employed for three methods to establish asymptotic tracking property of the closed loop teleoperation systems.The stability analysis is derived for both symmetrical and unsymmetrical time varying delays in the forward and backward communication channel that connects the local and remote sites.Finally,evaluation results are presented to illustrate the efectiveness of the proposed design for real-time applications.

A blind separation method of overlapped multi-components based on time varying AR model

A method utilizing single channel recordings to blindly separate the multicomponents overlapped in time and frequency domains is proposed in this paper. Based on the time varying AR model, the instantaneous frequency and amplitude of each signal component are estimated respectively, thus the signal component separation is achieved. By using prolate spheroidal sequence as basis functions to expand the time varying parameters of the AR model, the method turns the problem of linear time varying parameters estimation to a linear time invariant parameter estimation problem, then the parameters are estimated by a recursive algorithm. The computation of this method is simple, and no prior knowledge of the signals is needed. Simulation results demonstrate validity and excellent performance of this method.

STABILITY OF TIME VARYING SINGULAR DIFFERENTIAL SYSTEMS WITH DELAY

In this paper, stability of time varying singular differential systems with delay is considered. Based on variation formula and Gronwall-Bellman integral inequality, we obtain the exponential estimation of the solution and the sufficient conditions under which the considered system is stable and exponentially asymptotically stable. These results will be very useful to further research on Roust stability and control design of uncertain singular control systems with delay.

Generalization of integral inequalities and (c_1,c_1) stability of neutral differential equations with time-varying delays

A uniform stability analysis is developed for a type of neutral delays differential equations which depend on more general nonlinear integral inequalities. Many original investigations and results are obtained. Firstly, generations of two integral nonlinear inequalities are presented, which are very effective in dealing with the complicated integro-differential inequalities whose variable exponents are greater than zero. Compared with existed integral inequalities, those proposed here can be applied to more complicated differential equations, such as time-varying delay neutral differential equations. Secondly, the notions of (ω, Ω) uniform stable and (ω, Ω) uniform asymptotically stable, especially (c1, c1) uniform stable and (c1, c1) uniform asymptotically stable, are presented. Sufficient conditions on about (c1, c1) uniform stable and (c1, c1) uniform asymptotically stable of time-varying delay neutral differential equations are established by the proposed integral inequalities. Finally, a complex numerical example is presented to illustrate the main results effectively. The above work allows to provide further applications on the proposed stability analysis and control system design for different nonlinear systems. © 2017 Beijing Institute of Aerospace Information.

Robust pre-specified time synchronization of chaotic systems by employing time-varying switching surfaces in the sliding mode control scheme

In the conventional chaos synchronization methods, the time at which two chaotic systems are synchronized, is usually unknown and depends on initial conditions. In this work based on Lyapunov stability theory a sliding mode controller with time-varying switching surfaces is proposed to achieve chaos synchronization at a pre-specified time for the first time. The proposed controller is able to synchronize chaotic systems precisely at any time when we want. Moreover, by choosing the time-varying switching surfaces in a way that the reaching phase is eliminated, the synchronization becomes robust to uncertainties and exogenous disturbances. Simulation results are presented to show the effectiveness of the proposed method of stabilizing and synchronizing chaotic systems with complete robustness to uncertainty and disturbances exactly at a pre-specified time.

Novel criteria for exponential synchronization of inner time-varying complex networks with coupling delay

This paper mainly investigates the exponential synchronization of an inner time-varying complex network with coupling delay. Firstly, the synchronization of complex networks is decoupled into the stability of the corresponding dynamical systems. Based on the Lyapunov function theory, some sufficient conditions to guarantee its stability with any given convergence rate are derived, thus the synchronization of the networks is achieved. Finally, the results are illustrated by a simple time-varying network model with a coupling delay. All involved numerical simulations verify the correctness of the theoretical analysis.

Dynamic Stability Analysis of Linear Time-varying Systems via an Extended Modal Identification Approach

The problem of linear time-varying（LTV） system modal analysis is considered based on time-dependent state space representations, as classical modal analysis of linear time-invariant systems and current LTV system modal analysis under the ＂frozen-time＂ assumption are not able to determine the dynamic stability of LTV systems. Time-dependent state space representations of LTV systems are first introduced, and the corresponding modal analysis theories are subsequently presented via a stabilitypreserving state transformation. The time-varying modes of LTV systems are extended in terms of uniqueness, and are further interpreted to determine the system＇s stability. An extended modal identification is proposed to estimate the time-varying modes, consisting of the estimation of the state transition matrix via a subspace-based method and the extraction of the time-varying modes by the QR decomposition. The proposed approach is numerically validated by three numerical cases, and is experimentally validated by a coupled moving-mass simply supported beam exper- imental case. The proposed approach is capable of accurately estimating the time-varying modes, and provides anew way to determine the dynamic stability of LTV systems by using the estimated time-varying modes.

Universal Adaptive Observerfor Nonlinear Time varying Deterministic Systems

Designing adaptive observers for MIMO nonlinear time varying deterministic systems is an open problem .Inthis papera novelsolutiontothis problem is given byuse ofa “strongtrackingfilter(STF)”.First,the STFis outlined,then sometechnicalpoints ofview to usethe STFas an adaptive observer are discussed .Finally, two typicalexamples are presentedtoillustrate the effectiveness ofthe proposed approach.

Mixed sensitivity H_∞ control for LTI systems with varying time delays

Designing a robust controller for a system with timevarying delays poses a major challenge. In this paper, we propose a method based on mixed sensitivity H∞ for the control of linear time invariant（LTI） systems with varying time delays. The time delay is assumed bounded and the upper bound is known. In the technique we propose, the delay affecting the plant to be controlled is treated as an unmodeled uncertainty（in form of multiplicative uncertainty）. That uncertainty is approximated and then an H∞based controller, for the plant represented by the multiplicative uncertainty and the nominal model, is calculated. The obtained H∞controller is used to control the LTI systems with varying time delays. Simulation examples are given to illustrate the effectiveness of the proposed method.

Delay and Its Time-derivative Dependent Robust Stability of Uncertain Neutral Systems with Saturating Actuators

This note concerns the problem of the robust stability of uncertain neutral systems with time-varying delay and saturating actuators. The system considered is continuous in time with norm bounded parametric uncertainties. By incorporating the free weighing matrix approach developed recently, some new delay-dependent stability conditions in terms of linear matrix inequalities （LMIs） with some tuning parameters are obtained. An estimate of the domain of attraction of the closed-loop system under a priori designed controller is proposed. The approach is based on a polytopic description of the actuator saturation nonlinearities and the Lyapunov- Krasovskii method. Numerical examples are used to demonstrate the effectiveness of the proposed design method.

ELAPSED TIME OF PERIODIC MOTION WITH NEGATIVE DAMPING

An initially periodic motion is gradually raised out of the potential well by the effect of negative damping. The elapsed time when the motion ceases to be periodic is obtained by multiple variable expansions. An example of a strictly nonlinear system shows the result has a good approximation and is easy to calculate.

Robust H_∞ Stabilization of Uncertain Linear Time-Delay System with Delayed/Undelayed State Feedback Controllers

This paper focuses on the H∞ controller design for linear systems with time-varying delays and norm-bounded parameter perturbations in the system state and control/disturbance. On the existence of delayed/undelayed full state feedback controllers, we present a sufficient condition and give a design method in the form of Riccati equation. The controller can not only stabilize the time-delay system, but also make the H∞ norm of the closed-loop system be less than a given bound. This result practically generalizes the related results in current literature.

Optimization of Time-Varying Parking Charges and Parking Supply in Networks with Multiple User Classes and Multiple Parking Facilities

The optimization of parking charges and parking supply over the time of a day is an important problem in the design of transportation networks. This paper presents a bilevel model to determine the optimal time-varying parking charges and parking supply in road networks with multiple user classes and different types of parking facilities. The upper level of the model aims to maximize the network net benefit in response to the parking charges and parking supply, whereas the lower level is a time-dependent network equilibrium problem with elastic demand. A descent-gradient-based solution algorithm is adapted to solve the model. The numerical results show that the implementation of time-varying parking charges and parking supply is useful to effectively cater to the time-varying demand with different parking needs. The model provides a powerful tool for strategically designing parking locations and evaluating various parking policies.