An exemplified semi-analytical approach to the transient sensitivity of nonlinear systems

The internal logic of a model formulated as coupled differential equations is severely challenged in cases where the value of a poorly determined parameter greatly affects the solution of the model. Thus, the sensitivity to parameter changes is essential in the verification stage of the modelling process. Since a sensitivity analysis points out where the influence of flaws in present knowledge has important consequences, a sensitivity analysis will also act as a guideline for future research.In the literature known to the author, the approach to sensitivity has been either by parameter changes or local linearization. However, neither of these approaches will indicate the role played by parameter interactions in sensitivity unless a prohibitively large number of simulations are carried out.The present paper shows a known analytical expression for the sensitivity to parameter changes in nonlinear systems. A simplified version of the expression is solved for a model of the biological processes in activated sludge with 10 state-variables and 31 parameters leading to the presentation of simulated sensitivity of sensitivity, i.e. to an estimate of parameter interactions in sensitivity. The result of the approach is demonstrated through a comparison of actual and predicted changes in solution. Finally, a proposal of the extent of a complete sensitivity analysis is given.     

Sensitivity analysis for parametric control problems with control-state constraints

Parametric nonlinear control problems subject to vector-valued mixed control-state constraints are investigated. The model perturbations are implemented by a parameter p of a Banach-space P. We prove solution differentiability in the sense that the optimal solution and the associated adjoint multiplier function are differentiable functions of the parameter. The main assumptions for solution differentiability are composed by regularity conditions and recently developed second-order sufficient conditions (SSC). The analysis generalizes the approach in [16, 20] and establishes a link between (1) shooting techniques for solving the associated boundary value problem (BVP) and (2) SSC. We shall make use of sensitivity results from finite-dimensional parametric programming and exploit the relationships between the variational system associated to BVP and its corresponding Riccati equation.Solution differentiability is the theoretical backbone for any numerical sensitivity analysis. A numerical example with a vector-valued control is presented that illustrates sensitivity analysis in detail.     

基于一类新的Picture模糊距离的VIKOR多属性决策方法

针对现有Picture模糊距离的不足。本文构建了一种带有参数的Picture模糊距离,该参数能够反映决策者的态度偏好。其次,将新距离拓展到多准则妥协解排序法(VIKOR)中,并利用新距离计算各备选方案的群体效益值和个体遗憾值进而获得决策结果。最后,通过算例验证所提决策方法的有效性和优点,并对参数进行灵敏度分析。     

A new method for parameter sensitivity estimation in structural reliability analysis

For the parameter sensitivity estimation with implicit limit state functions in the time-invariant reliability analysis, the common Monte Carlo simulation based approach involves multiple trials for each parameter being varied, which will increase associated computational cost and the cost may become inevitably high especially when many random variables are involved. Another effective approach for this problem is featured as constructing the equivalent limit state function (usually called response surface) and performing the estimation in FORM/SORM. However, as the equivalent limit state function is polynomial in the traditional response surface method, it is not a good approximation especially for some highly non-linear limit state functions. To solve the above two problems, a new method, support vector regression based response surface method, is therefore presented in this paper. The support vector regression algorithm is employed to construct the equivalent limit state function and FORM/SORM is used in the parameter sensitivity estimation, and then two illustrative examples are given. It is shown that the computational cost of the sensitivity estimation can be greatly reduced and the accuracy can be retained, and results of the sensitivity estimation obtained by the proposed method are in satisfactory agreement with those computed by the conventional Monte Carlo methods.     

Two classes of sub-optimal shapes for one dimensional slider bearings with couple stress lubricants

Shape optimization of slider bearings operating with couple stress lubricants is performed here for the first time by using a novel direct optimal control approach, which defines the gradient of the film height as a control. The bearing load is maximized. One dimensional Reynolds and energy equations are used. Several constraints are taken into consideration. They avoid the occurrence of cavitation and ensure the validity of the Reynolds equation. The model is validated against a known analytical solution (the Rayleigh step bearing). Two simple design rules are inferred, which yield two different classes of sub-optimal shapes: the multi-stepped bearings and the multi-sloped bearings, respectively. Multi-stepped bearings consist of several steps and the couple stress parameter may affect the constant value of the film height between steps. Multi-sloped bearings consist of several inclined regions and the couple stress fluid parameter may affect the constant value of the film height between regions. The slider bearings operation under variable load is stable. A sensitivity analysis identified the design parameters which have the highest impact on bearing performance. The optimal slider bearing shapes obtained for Newtonian lubricants do not change when most common couple stress fluids are used. Isothermal models may be used successfully at lower values of the couple stress parameter.     

Interval static analysis of multi-cracked beams with uncertain size and position of cracks

This paper deals with beams under static loads, in presence of multiple cracks with uncertain parameters. The crack is modelled as a linearly-elastic rotational spring and, following a non-probabilistic approach, both stiffness and position of the spring are taken as uncertain-but-bounded parameters.A novel approach is proposed to compute the bounds of the response. The key idea is a preliminary monotonicity test, which evaluates sensitivity functions of the beam response with respect to the separate variation of every uncertain parameter within the pertinent interval. Next, two alternative procedures calculate lower and upper bounds of the response. If the response is monotonic with respect to all the uncertain parameters, the bounds are calculated by a straightforward sensitivity-based method making use of the sensitivity functions built in the monotonicity test. In contrast, if the response is not monotonic with respect to even one parameter only, the bounds are evaluated via a global optimization technique. The presented approach applies for every response function and the implementation takes advantage of closed analytical forms for all response variables and related sensitivity functions.Numerical results prove efficiency and robustness of the approach, which provides very accurate bounds even for large uncertainties, avoiding the computational effort required by the vertex method and Monte Carlo simulation.     

Parameter Identification for Nonlinear Abstract Cauchy Problems Using Quasilinearization

A quasilinearization approach to parameter identification in nonlinear abstract Cauchy problems in which the parameter appears in the nonlinear term, is presented. This approach has two main advantages over the classical one: it is much more intuitive and the derivation of the algorithm is done without need of the sensitivity equations on which classical quasilinearization is based. Sufficient conditions for the convergence of the algorithm are derived in terms of the regularity of the solutions with respect to the parameters. A comparison with the standard approach is presented and an application is included in which the nonphysical parameters in a mathematical model for shape memory alloys are estimated.     

A novel approach to safety stock management in a coordinated supply chain with controllable lead time using present value

This paper considers the management of safety stock in a coordinated single‐vendor single‐buyer supply chain under continuous review and Gaussian lead‐time demand. The lead time is supposed controllable, and shortages are not allowed. We follow the present value criterion by considering both inflation and time value of money. Our aim is to present a novel approach to optimizing the safety stock in such system. Under the conditions considered, the safety stock is typically determined according to the value assigned to the safety factor, which is thus treated as a parameter of the model, and not as a decision variable. In this paper, we take a different perspective by putting the order quantity and the safety factor in functional dependence through the adoption of a specific parameter. More precisely, we express the service level as a function of the number of admissible stockouts per time unit and the order quantity. This allows optimizing the safety stock taking into account the constraint on the number of admissible stockouts per time unit. We present both exact and approximated minimization algorithms. Numerical examples are finally shown to illustrate the effectiveness of the approximation algorithm, and to investigate the sensitivity of the model with respect to variations in some fundamental parameters. Copyright © 2015 John Wiley & Sons, Ltd.     

Two-warehouse production model for deteriorating inventory items with stock-dependent demand under inflation over a random planning horizon

This paper develops a production-inventory model for a deteriorating item with stock-dependent demand under two storage facilities over a random planning horizon, which is assumed to follow exponential distribution with known parameter. The effects of learning in set-up, production, selling and reduced selling is incorporated. Different inflation rates for various inventory costs and time value of money are also considered. A hybrid genetic algorithm is designed to solve the optimization problem which is hard to solve with existing algorithms due to the complexity of the decision variable. To illustrate the model and to show the effectiveness of the proposed approach a numerical example is provided. A sensitivity analysis of the optimal solution with respect to the parameters of the system is carried out.     

Online state of charge estimation of Li-ion battery based on an improved unscented Kalman filter approach

An improved unscented Kalman filter approach is proposed to enhance online state of charge estimation in terms of both accuracy and robustness. The goal is to address the drawback associated with the unscented Kalman filter in terms of its requirement for an accurate model and a priori noise statistics. Firstly, Li-ion battery modelling and offline parameter identification is performed. Secondly, a sensitivity analysis experiment is designed to verify which model parameter has the greatest influence on state of charge estimation accuracy, in order to provide an appropriate parameter for the model adaptive algorithm. Thirdly, an improved unscented Kalman filter approach, composed of a model adaptive algorithm and a noise adaptive algorithm, is introduced. Finally, the results are discussed, which reveal that the proposed approach’s estimation error is less than 1.79% with acceptable robustness and time complexity.     

具有时滞的单模激光系统的Hopf分支的频域分析

运用频域法研究了一类具有时滞的单模激光系统,选择时滞τ作为参数,当τ通过某个临界值时,Hopf分支产生,即从平衡点处分支出一簇周期解,最后,利用数值模拟证实理论分析结果的正确性.     

Singular Perturbation Analysis of Boundary Value Problems for Differential-Difference Equations. IV. A Nonlinear Example with Layer Behavior

A study is made of boundary value problems for a class of singularly perturbed nonlinear, second-order, differential-difference equations, i.e., where the highest-order derivative is multiplied by a small parameter. Depending on the region of parameter space, solutions of the nonlinear problem may not be unique, can exhibit extreme sensitivity to the values of the parameters, or may not exist. Typically, solutions exhibit layer behavior and/or exponentially large amplitudes. Approximate solutions of these boundary value problems are obtained by using singular perturbation methods and numerical computations and are then compared. Numerical computations of representative solutions illustrate the wide variety of possible behaviors.     

Optimal selection from distributions with unknown parameters: Robustness of Bayesian models

We consider the problem of making one choice from a known number of i.i.d. alternatives. It is assumed that the distribution of the alternatives has some unknown parameter. We follow a Bayesian approach to maximize the discounted expected value of the chosen alternative minus the costs for the observations. For the case of gamma and normal distribution we investigate the sensitivity of the solution with respect to the prior distributions. Our main objective is to derive monotonicity and continuity results for the dependence on parameters of the prior distributions. Thus we prove some sort of Bayesian robustness of the model.     

Stability on finite time interval and time-dependent bifurcation analysis of Duffing's equations

The concept of stability on finite time interval is proposed and some stability theorems are established. The delayed bifurcation transition of Dufflng's equations with a time-dependent parameter is analyzed. Function is used to predict the bifurcation transition value. The sensitivity of the solutions to initial values and parameters is also studied.     

Sensitivity analysis of temperature field in the heated soft tissue with respect to the perturbations of porosity

The thermal processes occurring in the heated soft tissues are described by generalized dual-phase lag model supplemented by appropriate boundary and initial conditions. A key parameter in this model is the porosity associated with the diameter of blood vessels and the diameter of the surrounding tissue. Using the direct approach, the sensitivity analysis of the tissue and blood temperatures with respect to these diameters is discussed. The basic problem and additional ones concerning the sensitivity functions are solved by means of the explicit scheme of finite difference method. In the final part the results of computations are presented and the conclusions are formulated.     

The conversion of Fredholm integral equations to equivalent cauchy problems

A new method is developed for converting various classes of Fredholm integral equations into equivalent initial value problems. In contrast with previous methods, which accomplished this by imbedding the equation, with respect to some parameter, in a family of similar ones, our approach is parameter free. To effect the conversion the integral equation is first shown to be equivalent to a two point boundary value problem. The application of various invariant imbedding algorithms completes the task. An extensive examination of linear equations is made, and it is shown that our procedure leads to a substantial reduction of dimensionality over previous methods. New techniques for solving critical length and continuation problems are another important consequence of our approach.     

Active Constraint Set Invariancy Sensitivity Analysis in Linear Optimization

Active constraint set invariancy sensitivity analysis is concerned with finding the range of parameter variation so that the perturbed problem has still an optimal solution with the same support set that the given optimal solution of the unperturbed problem has. However, in an optimization problem with inequality constraints, active constraint set invariancy sensitivity analysis aims to find the range of parameter variation, where the active constraints in a given optimal solution remains invariant.For the sake of simplicity, we consider the primal problem in standard form and consequently its dual may have an optimal solution with some active constraints. In this paper, the following question is answered: “what is the range of the parameter, where for each parameter value in this range, a dual optimal solution exists with exactly the same set of positive slack variables as for the current dual optimal solution?”. The differences of the results between the linear and convex quadratic optimization problems are highlighted too.     

Estimation of the L-Curve via Lanczos Bidiagonalization

The L-curve criterion is often applied to determine a suitable value of the regularization parameter when solving ill-conditioned linear systems of equations with a right-hand side contaminated by errors of unknown norm. However, the computation of the L-curve is quite costly for large problems; the determination of a point on the L-curve requires that both the norm of the regularized approximate solution and the norm of the corresponding residual vector be available. Therefore, usually only a few points on the L-curve are computed and these values, rather than the L-curve, are used to determine a value of the regularization parameter. We propose a new approach to determine a value of the regularization parameter based on computing an L-ribbon that contains the L-curve in its interior. An L-ribbon can be computed fairly inexpensively by partial Lanczos bidiagonalization of the matrix of the given linear system of equations. A suitable value of the regularization parameter is then determined from the L-ribbon, and we show that an associated approximate solution of the linear system can be computed with little additional work.     

Optimal mode-switching for hybrid systems with varying initial states

This paper concerns a particular aspect of the optimal control problem for switched systems that change modes whenever the state intersects certain switching surfaces. These surfaces are assumed to be parameterized by a finite dimensional switching parameter, and the optimization problem we consider is that of minimizing a given cost-functional with respect to the switching parameter under the assumption that the initial state of the system is not a priori known. We approach this problem from two different vantage points by first minimizing the worst possible cost over the given set of initial states using results from min–max optimization. The second approach is based on a sensitivity analysis in which variational arguments give the derivative of the switching parameters with respect to the initial conditions.