Blending Scheduling under Uncertainty Based on Particle Swarm Optimization Algorithm

Blending is an important unit operation in process industry. Blending scheduling is nonlinear optimiza- tion problem with constraints. It is difficult to obtain optimum solution by other general optimization methods. Particle swarm optimization (PSO) algorithm is developed for nonlinear optimization problems with both contin- uous and discrete variables. In order to obtain a global optimum solution quickly, PSO algorithm is applied to solve the problem of blending scheduling under uncertainty. The calculation results based on an example of gasoline blending agree satisfactory with the ideal values, which illustrates that the PSO algorithm is valid and effective in solving the blending scheduling problem.

Consistency and Asymptotic Property of a Weighted Least Squares Method for Networked Control Systems

In this paper, we study the problems related to parameter estimation of a single-input and single-output networked control system, which contains possible network-induced delays and packet dropout in both of sensor-to-controller path and controller-to-actuator path. A weighted least squares(WLS) method is designed to estimate the parameters of plant, which could overcome the data uncertainty problem caused by delays and dropout. This WLS method is proved to be consistent and has a good asymptotic property. Simulation examples are given to validate the results.

AN INTRUSION DETECTION SYSTEM BASED ON EVIDENCE THEORY AND ROUGH SET THEORY

In this paper,we propose a novel Intrusion Detection System (IDS) architecture utilizing both the evidence theory and Rough Set Theory (RST). Evidence theory is an effective tool in dealing with uncertainty question. It relies on the expert knowledge to provide evidences,needing the evidences to be independent,and this make it difficult in application. To solve this problem,a hybrid system of rough sets and evidence theory is proposed. Firstly,simplification are made based on Variable Precision Rough Set (VPRS) conditional entropy. Thus,the Basic Belief Assignment (BBA) for all evidences can be calculated. Secondly,Dempster’s rule of combination is used,and a decision-making is given. In the proposed approach,the difficulties in acquiring the BBAs are solved,the correlativity among the evidences is reduced and the subjectivity of evidences is weakened. An illustrative example in an intrusion detection shows that the two theories combination is feasible and effective.

Comprehensive Evaluation Model of Reservoir Operation Based on Improved Set Pair Analysis

A comprehensive evaluation model based on improved set pair analysis is established. Considering the complexity in decision-making process, the model combines the certainties and uncertainties in the schemes, i.e., identical degree, different degree and opposite degree. The relations among different schemes are studied, and the traditional way of solving uncertainty problem is improved. By using the gray correlation to determine the difference degree, the problem of less evaluation indexes and inapparent linear relationship is solved. The difference between the evaluation parameters is smaller in both the fuzzy comprehensive evaluation model and fuzzy matter-element method, and the dipartite degree of the evaluation result is unobvious. However, the difference between each integrated connection degree is distinct in the improved set pair analysis. Results show that the proposed method is feasible and it obtains better effects than the fuzzy comprehensive evaluation method and fuzzy matter-element method.

Direct adaptive control for lane keeping in intelligent vehicle systems

In this paper, with parametric uncertainties such as the mass of vehicle, the inertia of vehicle about vertical axis, and the tire cornering stiffness, we deal with the vehicle lateral control problem in intelligent vehicle systems. Based on the dynamical model of vehicle, by applying Lyapunov function method, the control problem for lane keeping in the presence of parametric uncertainty is studied, the direct adaptive algorithm to compensate for parametric variations is proposed and the terminal sliding mode variable structure control laws are designed with look-ahead references systems. The stability of the system is investigated from the zero dynamics analysis. Simulation results show that convergence rates of the lateral displacement error, yaw angle error and slid angle are fast.

A Non-canonical Example to Support P Is Not Equal to NP

The more unambiguous statement of the P versus NP problem and the judgement of its hardness, are the key ways to find the full proof of the P versus NP problem. There are two sub-problems in the P versus NP problem. The first is the classifications of different mathematical problems (languages), and the second is the distinction between a non-deterministic Turing machine (NTM) and a deterministic Turing machine (DTM). The process of an NTM can be a power set of the corresponding DTM, which proves that the states of an NTM can be a power set of the corresponding DTM. If combining this viewpoint with Cantor's theorem, it is shown that an NTM is not equipotent to a DTM. This means that "generating the power set P(A) of a set A" is a non-canonical example to support that P is not equal to NP.

Similarity measure design on overlapped and non-overlapped data

Similarity measure design on non-overlapped data was carried out and compared with the case of overlapped data.Unconsistant feature of similarity on overlapped data to non-overlapped data was provided by example.By the artificial data illustration,it was proved that the conventional similarity measure was not proper to calculate the similarity measure of the non-overlapped case.To overcome the unbalance problem,similarity measure on non-overlapped data was obtained by considering neighbor information.Hence,different approaches to design similarity measure were proposed and proved by consideration of neighbor information.With the example of artificial data,similarity measure calculation was carried out.Similarity measure extension to intuitionistic fuzzy sets(IFSs)containing uncertainty named hesitance was also followed.

Study on Factors of College Student＇s Network service time Based on Bayesian Networks

Due to the uncertainty of the factors that influence the network service time and other characters of college student, Bayesian Network is used to model this kind of system. Different algorithms are used for learning Bayesian Networks in order to compare several models. It is suggested that researchers can use Bayesian Networks to explore the potential relationship between variables of complex social problems. The result indicates that learning target and family closeness degree are the key variables which influenced college student＇ s network service time. Origin of student and family economy didn＇ t influence college student＇ s network service time directly. Schools and community should strengthen the education of college students life planning and communication with parents.

Soil Uncertainties on Performance of Geotechnical Works

Performance-Based Design （PBD） is a more rational approach, particularly in seismic environments. In this approach it is relevant the performance required to structures and to geotechnical works, as well as the geotechnical constitutive models used to predict the performance. The parameters of the constitutive models are related in turn to soil properties. So soil properties are a key point for Performance-Based Design. Questions arising are： （i） which are the more relevant soil properties to solve a specific PBD geotechnical problem？ （ii） which are the more relevant model parameters and how they can be evaluated and/or correlated to soil properties？ （iii） which is the role of the soil parameters uncertainty in Performance-Based Design？ An answer to these questions is given in this paper, outlining the potential offered by the new advanced in-situ and laboratory tests and discussing the performance required by some geotechnical works.

Algebraic solution of differential geometric guidance command and time delay control

According to the three-dimensional geometry of the engagement,the explicit algebraic expression of differential geometric guidance command(DGGC)is proposed.Compared with the existing solutions,the algebraic solution is much simpler and better for the further research of the characteristics of DGGC.Time delay control(TDC)is a useful method to tackle the uncertainty problem of a control system.Based on TDC,taking the target maneuvering acceleration as a disturbance,the estimation algorithm of the target maneuvering acceleration is presented,which can be introduced in DGGC to improve its performance.Then,the augmented DGGC(ADGGC)is obtained.The numerical simulation of intercepting a high maneuvering target is conducted to demonstrate the effectiveness of ADGGC.

Stability control of steer by wire system based on μ synthesis robust control

The uncertainty influences may result in performance deterioration and instability to the steer by wire(SBW) system. Thus, it must make the control system keep robust stability from uncertainty, and have good robustness. In order to effectively restrain the interference and improve steering stability, this paper presents a μ synthesis robust controller based on SBW system, which considers the effect of model uncertainty and external disturbance on the system dynamics. Taking the ideal yaw rate tracking, interference suppression and excellent robustness as the control objectives, the μ synthesis robust controller is designed using linear fractional transformation theory to deal with the uncertainty. Then, it is testified through time domain and robustness simulation analysis. Simulation results show that the proposed controller can not only ensure robustness and robust stability of the system quite well, but improve handling stability of the vehicle effectively. The results of this study provide certain theoretical basis for the research and application of SBW system.

Response-surface-based structural reliability analysis with random and interval mixed uncertainties

Traditional reliability analysis requires probability distributions of all the uncertain parameters.However,in many practical applications,the variation bounds can be only determined for the parameters with limited information.A complex hybrid reliability problem then will be caused when the random and interval variables coexist in a same structure.In this paper,by introducing the response surface technique,we develop a new hybrid reliability method to efficiently compute the interval of the failure probability of the structure due to the probability-interval hybrid uncertainty.The present method consists of a sequence of iterations.At each step,a response surface model is constructed for the limit-state function by using a quadratic polynomial and a modified axial experimental design method.An approximate hybrid reliability problem is created based on the response surface model,which is subsequently solved by an efficient decoupling approach.An updating strategy is suggested to improve the quality of the response surface and whereby ensure the reliability analysis precision.A computational procedure is then summarized for the whole iterations.Four numerical examples and also a practical application are provided to demonstrate the effectiveness of the present method.

An iterative interval analysis method based on Kriging-HDMR for uncertainty problems

In recent years,growing attention has been paid to the interval investigation of uncertainty problems.However,the contradiction between accuracy and efficiency always exists.In this paper,an iterative interval analysis method based on Kriging-HDMR(IIAMKH)is proposed to obtain the lower and upper bounds of uncertainty problems considering interval variables.Firstly,Kriging-HDMR method is adopted to establish the meta-model of the response function.Then,the Genetic Algorithm&Sequential Quadratic Programing(GA&SQP)hybrid optimization method is applied to search for the minimum/maximum values of the meta-model,and thus the corresponding uncertain parameters can be obtained.By substituting them into the response function,we can acquire the predicted interval.Finally,an iterative process is developed to improve the accuracy and stability of the proposed method.Several numerical examples are investigated to demonstrate the effectiveness of the proposed method.Simulation results indicate that the presented IIAMKH can obtain more accurate results with fewer samples.

Indifference pricing and hedging in a multiple-priors model with trading constraints

This paper considers utility indifference valuation of derivatives under model uncertainty and trading constraints, where the utility is formulated as an additive stochastic differential utility of both intertemporal consumption and terminal wealth, and the uncertain prospects are ranked according to a multiple-priors model of Chen and Epstein(2002). The price is determined by two optimal stochastic control problems(mixed with optimal stopping time in the case of American option) of forward-backward stochastic differential equations.By means of backward stochastic differential equation and partial differential equation methods, we show that both bid and ask prices are closely related to the Black-Scholes risk-neutral price with modified dividend rates.The two prices will actually coincide with each other if there is no trading constraint or the model uncertainty disappears. Finally, two applications to European option and American option are discussed.

Robust flutter analysis based on genetic algorithm

Robust flutter analysis considering model uncertain parameters is very important in theory and engineering applications.Modern robust flutter solution based on structured singular value subject to real parametric uncertainties may become difficult because the discontinuity and increasing complexity in real mu analysis.It is crucial to solve the worst-case flutter speed accurately and efficiently for real parametric uncertainties.In this paper,robust flutter analysis is formulated as a nonlinear programming problem.With proper nonlinear programming technique and classical flutter analysis method,the worst-case parametric perturbations and the robust flutter solution will be captured by optimization approach.In the derived nonlinear programming problem,the parametric uncertainties are taken as design variables bounded with perturbed intervals,while the flutter speed is selected as the objective function.This model is optimized by the genetic algorithm with promising global optimum performance.The present approach avoids calculating purely real mu and makes robust flutter analysis a plain job.It is illustrated by a special test case that the robust flutter results coincide well with the exhaustive method.It is also demonstrated that the present method can solve the match-point robust flutter solution under constant Mach number accurately and efficiently.This method is implemented in problem with more uncertain parameters and asymmetric perturbation interval.

ROBUST OUTPUT FEEDBACK STABILIZATION OF NONLINEAR NETWORKED SYSTEMS VIA A FINITE DATA-RATE COMMUNICATION CHANNEL

This paper addresses a robust stabilization problem of a class of uncertain nonlinear systems using output measurements via a finite data-rate communication channel. The authors assumes that there exist an observer and a control law for the systems in the absence of any finite data-rate communi- cation channel. Based on the observer and the control law, the authors constructs an encoder/decoder pair and provides a sufficient condition, including suitable sampling period and data rate, which will guarantee the stability of the closed-loop systems when a finite data-rate communication channel is introduced.

Robust linear optimization under matrix completion

Linear programming models have been widely used in input-output analysis for analyzing the interdependence of industries in economics and in environmental science.In these applications,some of the entries of the coefficient matrix cannot be measured physically or there exists sampling errors.However,the coefficient matrix can often be low-rank.We characterize the robust counterpart of these types of linear programming problems with uncertainty set described by the nuclear norm.Simulations for the input-output analysis show that the new paradigm can be helpful.

Information Revelation in Sequential Auctions with Uncertainties About Future Objects

In many auctions,buyers know beforehand little about objects to be sold in the future.Whether and how to reveal information about future objects is an important decision problem for sellers.In this paper,two objects are sold sequentially and each buyer's valuation for the second object is k times that for the first one,and the true value of k is sellers' private information.The authors identify three factors which affect sellers' revelation strategies: The market's competition intensity which is characterized by the number of buyers,buyers' prior information about the second object,and the difference degree between two objects which is characterized by k.The authors give not only conditions under which revealing information about the second object in advance benefits the seller,but also the optimal releasing amount of information in the market with two sellers and one seller,respectively.

Evolutionary algorithm based on dynamical structure of membrane systems in uncertain environments

In this paper, a new evolutionary algorithm based on a membrane system is proposed to solve the dynamic or uncertain optimization problems. The proposed algorithm employs objects, a dynamical membrane structure and several reaction rules of the membrane systems. The object represents a candidate solution of the optimization problems. The dynamical structure consists of the nested membranes where a skin membrane contains several membranes, which is useful for the proposed algorithm that finds optimal solutions. The reaction rules are designed to locate and track the optimal solutions of the dynamic optimization problems （DOPs）, which are inspired by processing the chemical compounds in the region of cellular membranes. Experimental study is conducted based on the moving peaks benchmark to evaluate the performance of the proposed algorithm in comparison with three state-of-the-art dynamic optimization algorithms. The results indicate the proposed algorithm is effective to solve the DOPs.