An improved interval model updating method via adaptive Kriging models

Interval model updating(IMU)methods have been widely used in uncertain model updating due to their low requirements for sample data.However,the surrogate model in IMU methods mostly adopts the one-time construction method.This makes the accuracy of the surrogate model highly dependent on the experience of users and affects the accuracy of IMU methods.Therefore,an improved IMU method via the adaptive Kriging models is proposed.This method transforms the objective function of the IMU problem into two deterministic global optimization problems about the upper bound and the interval diameter through universal grey numbers.These optimization problems are addressed through the adaptive Kriging models and the particle swarm optimization(PSO)method to quantify the uncertain parameters,and the IMU is accomplished.During the construction of these adaptive Kriging models,the sample space is gridded according to sensitivity information.Local sampling is then performed in key subspaces based on the maximum mean square error(MMSE)criterion.The interval division coefficient and random sampling coefficient are adaptively adjusted without human interference until the model meets accuracy requirements.The effectiveness of the proposed method is demonstrated by a numerical example of a three-degree-of-freedom mass-spring system and an experimental example of a butted cylindrical shell.The results show that the updated results of the interval model are in good agreement with the experimental results.

Theoretical analysis of non-probabilistic reliability based on interval model

The aim of this paper is to propose a theoretical approach for performing the nonprobabilistic reliability analysis of structure.Due to a great deal of uncertainties and limited measured data in engineering practice,the structural uncertain parameters were described as interval variables.The theoretical analysis model was developed by starting from the 2-D plane and 3-D space.In order to avoid the loss of probable failure points,the 2-D plane and 3-D space were respectively divided into two parts and three parts for further analysis.The study pointed out that the probable failure points only existed among extreme points and root points of the limit state function.Furthermore,the low-dimensional analytical scheme was extended to the high-dimensional case.Using the proposed approach,it is easy to find the most probable failure point and to acquire the reliability index through simple comparison directly.A number of equations used for calculating the extreme points and root points were also evaluated.This result was useful to avoid the loss of probable failure points and meaningful for optimizing searches in the research field.Finally,two kinds of examples were presented and compared with the existing computation.The good agreements show that the proposed theoretical analysis approach in the paper is correct.The efforts were conducted to improve the optimization method,to indicate the search direction and path,and to avoid only searching the local optimal solution which would result in missed probable failure points.

Optimization of uncertain acoustic metamaterial with Helmholtz resonators based on interval model

In summary,the interval uncertainty is introduced to the acoustic metamaterial with Helmholtz resonators.And then,new descriptions(the conservative approximation,the unsafe approximation and the approximation precision)on uncertainties of physical properties of this interval acoustic metamaterial are defined.Lastly,an optimization model for this interval acoustic metamaterial is proposed.The organization of this paper is listed as follows.The acoustic transmission line method(ATLM)for an acoustic metamaterial with Helmholtz resonators is described in Section 2.In Section3,uncertain analysis of the interval acoustic metamaterial is presented.In Section 4,optimization model of the interval acoustic metamaterial is proposed.The discussion on optimization results is shown in Section 5.In section 6,some conclusions are given.

Optimal harvest of an interval model of carbon sink fisheries with multi-trophic levels

Considering the uncertainty of kelp-abalone-sea cucumber population, an interval model of carbon sink fisheries with multi-trophic levels is proposed. The equilibria of the model are identified and the corresponding stabilities are discussed. And the existence of bionomic equilibrium of the model is investigated. Next the optimal controller is designed to obtain the optimal harvest using Pontryagin＇s maximum principle. Numerical simulations are carried to prove the results.

Interval standard neural network models for nonlinear systems

A neural-network-based robust control design is suggested for control of a class of nonlinear systems. The design ap- proach employs a neural network, whose activation functions satisfy the sector conditions, to approximate the nonlinear system. To improve the approximation performance and to account for the parameter perturbations during operation, a novel neural network model termed standard neural network model (SNNM) is proposed. If the uncertainty is bounded, the SNNM is called an interval SNNM (ISNNM). A state-feedback control law is designed for the nonlinear system modelled by an ISNNM such that the closed-loop system is globally, robustly, and asymptotically stable. The control design equations are shown to be a set of linear matrix inequalities (LMIs) that can be easily solved by available convex optimization algorithms. An example is given to illustrate the control design procedure, and the performance of the proposed approach is compared with that of a related method reported in literature.

An EMD based method for detrending RR interval series without resampling

Slow trends in the RR interval(RRI) series should be removed in the preprocessing step to get a reliable result of heart rate variability(HRV) analysis. Re-sampling is required to convert the unevenly sampled RRI series into evenly sampled time series when using the widely accepted smoothness priors approach(SPA). Noise is introduced in this process and the information quality is thus compromised. Empirical mode decomposition(EMD) and its variants, were introduced to directly process the unevenly sampled RRI series. Besides, a RR interval model was proposed to fascinate the introduction of standard metrics for the evaluation of the detrending performance. Based on standard metrics including signal-to-noise-ratio in d B(ISNR), mean square error(EMS), and percent root square difference(DPRS), the effectiveness of detrending methods in RR interval analysis were determined. Results demonstrate that complementary ensemble EMD(CEEMD, a variant of EMD) based method has a higher ISNR, a lower EMS and a lower DPRS as well as a better RRI series detrending performance compared with the SPA method, which would in turn lead to a more accurate HRV analysis.

Risk assessment of water inrush in tunnels based on attribute interval recognition theory

Water inrush is one of the most serious geological hazards in underground engineering construction.In order to effectively prevent and control the occurrence of water inrush,a new attribute interval recognition theory and method is proposed to systematically evaluate the risk of water inrush in karst tunnels.Its innovation mainly includes that the value of evaluation index is an interval rather than a certain value;the single-index attribute evaluation model is improved non-linearly based on the idea of normal distribution;the synthetic attribute interval analysis method based on improved intuitionistic fuzzy theory is proposed.The TFN-AHP method is proposed to analyze the weight of evaluation index.By analyzing geological factors and engineering factors in tunnel zone,a multi-grade hierarchical index system for tunnel water inrush risk assessment is established.The proposed method is applied to ventilation incline of Xiakou tunnel,and its rationality and practicability is verified by comparison with field situation and evaluation results of other methods.In addition,the results evaluated by this method,which considers that water inrush is a complex non-linear system and the geological conditions have spatial variability,are more accurate and reliable.And it has good applicability in solving the problem of certain and uncertain problem.

A Hybrid Inexact Optimization Model for Land-use Allocation of China

Land scarcity has become the prominent obstacle on the way to sustainable development for China. Under the constraints of land shortage, how to allocate the finite land resources to the multiple land users in China considering various political, environmental, ecological and economic conditions have become research topics with great significance. In this study, an interval fuzzy national-scale land-use model(IFNLM) was developed for optimizing land systems of China. IFNLM is based on an integration of existing interval linear programming(ILP), and fuzzy flexible programming(FFP) techniques. IFNLM allows uncertainties expressed as discrete interval values and fuzzy sets to be incorporated within a general optimization framework. It can also facilitate national-scale land-use planning under various environmental, ecological, social conditions within a multi-period and multi-option context. Then, IFNLM was applied to a real case study of land-use planning in China. The satisfaction degree of environmental constraints is between 0.69 and 0.97, the system benefit will between 198.25 × 1012 USD and 229.67 × 1012 USD. The results indicated that the hybrid model can help generate desired policies for land-use allocation with a maximized economic benefit and minimized environmental violation risk. Optimized land-use allocation patterns can be generated from the proposed IFNLM.

Study on conveyor natural frequency and time intervalfor switching off resistances

A conveyor belt driven by wound rotor motors produces dynamic tension, velocity and accelerationduring starting. The terrible situation (such as resonance) in dynamic analysis and design is that system naturalfrequencies are equal to those for switching off electric resistances. This paper analyzes and determines systemnatural frequencies based on a modeling method of receptances with the analysis of sub-systems model and of theprinciple of their addition and conveyor loop closure. It also puts forward to calculate the time interval for switching off electric resistances. The starting of one conveyor is simulated by lumped-mass-spring-model software tofurther illustrate the influence of time interval for switching off electric resistances on conveyor dynamic behavior. Two methods are also compared. The receptance model is proved to be an excellent alternative.

Prediction of a maximum pull-out load of anchor bolts using an optimal combination model

The mixed model of improved exponential and power function and unequal interval gray GM(1,1)model have poor accuracy in predicting the maximum pull-out load of anchor bolts.An optimal combination model was derived using the optimally weighted combination theory and the minimum sum of logarithmic squared errors as the objective function.Two typical anchor bolt pull-out engineering cases were selected to compare the performance of the proposed model with those of existing ones.Results showed that the optimal combination model was suitable not only for the slow P-s curve but also for the steep P-s curve.Its accuracy and stable reliability,as well as its prediction capability classification,were better than those of the other prediction models.Therefore,the optimal combination model is an effective processing method for predicting the maximum pull-out load of anchor bolts according to measured data.

Inference for Gompertz distribution under records

The parameter estimation is considered for the Gompertz distribution under frequensitst and Bayes approaches when records are available.Maximum likelihood estimators,exact and approximate confidence intervals are developed for the model parameters,and Bayes estimators of reliability performances are obtained under different losses based on a mixture of continuous and discrete priors.To investigate the performance of the proposed estimators,a record simulation algorithm is provided and a numerical study is presented by using Monte-Carlo simulation.

Active fault tolerant control design for stochastic Interval Type-2 Takagi-Sugeno fuzzy model

Purpose-The purpose of this paper is to look at the problem of fault tolerant control(FTC)for discrete time nonlinear system described by Interval Type-2 Takagi–Sugeno(IT2 TS)fuzzy model subjected to stochastic noise and actuator faults.Design/methodology/approach–An IT2 fuzzy augmented state observer is first developed to estimate simultaneously the system states and the actuator faults since this estimation is required for the design of the FTC control law.Furthermore,based on the information of the states and the faults estimate,an IT2 fuzzy state feedback controller is conceived to compensate for the faults effect and to ensure a good tracking performance between the healthy system and the faulty one.Sufficient conditions for the existence of the IT2 fuzzy controller and the IT2 fuzzy observer are given in terms of linear matrix inequalities which can be solved using a two-step computing procedure.Findings–The paper opted for simulation results which are applied to the three-tank system.These results are presented to illustrate the effectiveness of the proposed FTC strategy.Originality/value–In this paper,the problem of active FTC design for noisy and faulty nonlinear system represented by IT2 TS fuzzy model is treated.The developed IT2 fuzzy fault tolerant controller is designed such that it can guarantee the stability of the closed-loop system.Moreover,the proposed controller allows to accommodate for faults,presents a satisfactory state tracking performance and outperforms the traditional type-1 fuzzy fault tolerant controller.

INTERVAL TIME SERIES ANALYSIS WITH AN APPLICATION TO THE STERLING-DOLLAR EXCHANGE RATE

Traditional econometrics has long employed ＂points＂ to measure time series data. In real life situations, however, it suffers the loss of volatility information, since many variables are bounded by intervals in a given period. To address this issue, this paper provides a new methodology for interval time series analysis. The concept of ＂interval stochastic process＂ is formally defined as a counterpart of ＂stochastic process＂ in point-based econometrics. The authors introduce the concepts of interval stationarity, interval statistics （including interval mean, interval variance, etc.） and propose an interval linear model to investigate the dynamic relationships between interval processes. A new interval-based optimization approach for estimation is proposed, and corresponding evaluation criteria are derived. To demonstrate that the new interval method provides valid results, an empirical example on the sterling-dollar exchange rate is presented.

An interval finite element method for electromagnetic problems with spatially uncertain parameters

During the manufacturing process of dielectric materials used in electromagnetic engineering, the electromagnetic parameters are often spatially uncertain due to the processing technology, environmental temperature, personal operations, etc. Traditionally,the random field model can be used to measure the spatial uncertainties, but its construction requires a large number of samples.On the contrary, the interval field model only needs the upper and lower bounds of the spatially uncertain parameters, which requires much less samples and furthermore is easy to understand and use for engineers. Therefore, in this paper, the interval field model is introduced to describe the spatial uncertainties of dielectric materials, and then an interval finite element method(IFEM) is proposed to calculate the upper and lower bounds of electromagnetic responses. Firstly, the interval field of the dielectric material is represented by the interval K-L expansion and inserted into the scalar Helmholtz wave equations, and thus the interval equilibrium equations are constructed according to the node-based finite element method. Secondly, a perturbation interval finite element method is developed for calculating the upper and lower bounds of electromagnetic responses such as the electric strength and magnetic strength. Finally, the effectiveness of the proposed method is verified by three numerical examples.

Interval Type-2 Fuzzy Passive Filtering for Nonlinear Singularly Perturbed PDT-Switched Systems and Its Application

The problem of designing a passive filter for nonlinear switched singularly perturbed systems with parameter uncertainties is explored in this paper.Firstly,the multiple-time-scale phenomenon is settled effectively by introducing a singular perturbation parameter in the plant.Secondly,the interval type-2 fuzzy set theory is employed where parameter uncertainties are expressed in membership functions rather than the system matrices.It is worth noting that interval type-2 fuzzy sets of the devised filter are different from the plant,which makes the design of the filter more flexible.Thirdly,the persistent dwell-time switching rule,as a kind of time-dependent switching rules,is used to manage the switchings among nonlinear singularly perturbed subsystems,and this rule is more general than dwell-time and average dwell-time switching rules.Next,sufficient conditions are provided for guaranteeing that the filtering error system is globally uniformly exponentially stable with a passive performance.Furthermore,on the basis of the linear matrix inequalities,the explicit expression of the designed filter can be obtained.Finally,a tunnel diode electronic circuit is rendered as an example to confirm the correctness and the validity of the developed filter.

Finite-Time Stability for Interval Type-2 Fuzzy Nonlinear Systems via an Observer-Based Sliding Mode Control

This work focuses on the design of a sliding mode controller for a class of continuoustime interval type-2 fuzzy-model-based nonlinear systems with unmeasurable state information over a finite-time interval.Aiming at describing the nonlinearities containing parameter uncertainties that inevitably appear in practice,the interval type-2 fuzzy sets are employed to model the studied system.To improve the designing flexibility,a fuzzy observer model non-parallel distribution compensation scheme is designed to estimate the state information of the plant,i.e.,the observer is allowed to have a mismatching premise structure from the system.On this basis,the appropriate fuzzy sliding surface and fuzzy controller are constructed by following the same premise variables as the designed fuzzy observer.Then,by means of the sliding mode control theory and the Lyapunov function method,some novel sufficient criteria are established to ensure the finite-time boundedness for the studied systems via a partitioning strategy including the reaching phase,the sliding motion phase and the whole time interval.Furthermore,the designed gains are acquired by solving the matrix convex optimization problem.Finally,the effectiveness of the developed method is demonstrated by two simulation examples.

Modelling medium-and long-term purchasing plans for environment-orientated container trucks:a case study of Yangtze River port

The transportation sector is the most significant contributor to anthropogenic greenhouse gas(GHG)emissions.Particularly,maritime transportation,which is predominantly powered by fossil-fuel engines,accounts for more than 90%of world freight movement and emits 3%of global carbon dioxide(CO_(2))emissions.China is the world’s largest emitter of CO_(2 )and plays a key role in mitigating global climate change.In order to tackle this pressing concern,this study analyses the port’s throughput,the current number of trucks and their emissions during the container truck purchasing process.Previous studies about container truck purchasing plans mostly focused on the trucks’price and port needs.The objective of this study is to minimize the total cost of a port’s inland transportation using optimization technique such as the interval uncertainty planning model to convert container truck emissions into social costs.The study considers the port of Yangtze as a case study.The study has designed two scenarios.(i)The base scenario(business-asusual,BAU)is used to quantify the relationship between pollutant emissions and system cost.In the base scenario,no environmental control facilities are used during the planning period,and there is no need to purchase new energy container trucks.(ii)The expected scenario(Scenario A)is for three planning periods.In Scenario A,the emissions levels are required to remain at the same level as the first planning period during the whole planning period.By solving the above model,the number of all truck types,system cost,container throughput and truck emissions in the port area were analysed.The results showed that if no emission reduction control measures are implemented in the next 9 years,the growth rate of pollutants in the port area could reach 20%.In addition,the findings showed clearly that truck emissions are reduced by purchasing new energy trucks and restricting the number of fossil-fuel(diesel)trucks.This study could also help to minimize system costs associated with port planning and management.

Finite-time non-fragile filtering for nonlinear networked control systems via a mixed time/event-triggered transmission mechanism

This paper is aimed at investigating the problem of mixed time/event-triggered finite-time non-fragile filtering for nonlinear networked control systems with delay.First,a fuzzy nonlinear networked control system model is established by interval type-2(IT2)Takagi-Sugeno(T-S)fuzzy model,the designed non-fragile filter resolves the filter parameter uncertainties and uses different membership functions from the IT2 T-S fuzzy model.Second,a novel mixed time/event-triggered transmission mechanism is proposed,which decreases the waste of network resources.Next,Bernoulli random variables are used to describe the cases of random switching mixed time/event-triggered transmission mechanism.Then,the error filtering system is designed by considering a Lyapunov function and a sufficient condition of finite-time boundedness.In addition,the existence conditions for the finite-time non-fragile filter are given by the linear matrix inequalities(LMIs).Finally,two simulation results are presented to prove the effectiveness of the obtained method.