随机利率和随机波动率下保险和再保险公司的稳健最优投资策略 （ 英文 ）

A robust optimal proportional reinsurance and investment management problem is studied fora general insurance company which holds shares of an insurance company and a reinsurance company. Boththe insurance company and reinsurance company are allowed to invest in a risk-free asset and a risky assetwith stochastic interest rate and stochastic volatility, in which the interest rate is described by an affinemodel. Besides, the general insurance company's manager is an ambiguity-averse manager who worriesabout model uncertainty. By employing the dynamic programming approach, the explicit formulae is derivedfor the optimal robust reinsurance-investment strategy and the optimal value function, and one special caseis discussed subsequently. Finally, a part of numerical examples is presented to illustrate the effects ofmodel parameters.     

Reliable grading robust stabilization for uncertain time-varying systems via dynamic compensator

A new general model for uncertain time-varying parameters and a new measure sensor failure model are presented, and the problems of both grading robust stabilization and reliable grading robust stabilization for such systems are studied. By the Lyapunov stability theory and matrix algebra method, some sufficient criteria for the above two control problems are established in quasi-linear matrix inequalities (Q-LMIS) forms. In view of linear matrix inequality (LMI) approach, a solving procedure for the Q-LMIS problem is proposed. The solvability of the Q-LMIS problem can be improved obviously by adding some LMI constraints to the Q-LMIS. Based on the two Q-LMIS criteria, a grading robust stable control strategy, namely, the controller with different energy is acted on the system with different uncertain parameter range, is presented. The numerical simulating results show that the grading robust stable control strategy for the robust stabilization of uncertain systems has important theoretical and practical significance.     

Improving Adaptive Learning Rate of BP Neural Network for the Modelling of 3D Woven Composites Using the Golden Section Law

Focused on various BP algorithms with variable learning rate based on network system error gradient, a modified learning strategy for training non-linear network models is developed with both the incremental and the decremental factors of network learning rate being adjusted adaptively and dynamically. The golden section law is put forward to build a relationship between the network training parameters, and a series of data from an existing model is used to tram and test the network parameters. By means of the evaluation of network performance in respect to convergent speed and predicting precision, the effectiveness of the proposed learning strategy can be illustrated.     

Support vector machine based on chaos particle swarm optimization for fault diagnosis of rotating machine

The performance of the support vector machine models depends on a proper setting of its parameters to a great extent. A novel method of searching the optimal parameters of support vector machine based on chaos particle swarm optimization is proposed. A multifault classification model based on SVM optimized by chaos particle swarm optimization is established and applied to the fault diagnosis of rotating machines. The results show that the proposed fault classification model outperforms the neural network trained by chaos particle swarm optimization and least squares support vector machine, and the precision and reliability of the fault classification results can meet the requirement of practical application. It indicates that chaos particle swarm optimization is a suitable method for searching the optimal parameters of support vector machine.     

A measurement error model for microarray data analysis

Microarray technology has been widely used to analyze the gene expression levels by detecting fluorescence intensity in a high throughput fashion. However, since the measurement error produced from various sources in microarray experiments is heterogeneous and too large to be ignored, we propose here a measurement error model for microarray data processing, by which the standard deviation of the measurement error is demonstrated to be linearly increased with fluorescence intensity. A robust algorithm, which estimates the parameters of the measurement error model from a single microarray without replicated spots, is provided. The model and algorithm for estimating of the parameters from a given data set are tested on both the real data set and the simulated data set, and the result has been proven satisfactory. And, combining the measurement error model with traditional Z-test method, a full statistical model has been developed. It can significantly improve the statistical inference for identifying differentially expressed genes.     

Broadcast Scheduling Strategy Based on the Priority of Real-Time Data in a Mobile Environment

Data broadcast is an important data dissemination approach in mobile environment. On broadcast channel, scalability and efficiency of data transmission are satisfied. In a mobile environment, there exists a kind of real-time database application in which both the transactions and data can have their timing constraints and priorities of different levels. In order to meet the requirement of real-time data disseminating and retrieving, a broadcast scheduling strategy HPF-ED F (Highest Priority First with Earlier Deadline and Frequency) is proposed under the BoD (Broadcast on Demand) model. Using the strategy, data items are scheduled according to their priority the transaction imposed on them or system set for them. The strategy also considers other characteristics of data items such as deadline and popularity of data. The extensive simulation experiments have been conducted to evaluate the performance of the proposed algorithm. Results show that it can achieve excellent performance compared with existing     

Broadcast Scheduling Strategy Based on the Priority of Real.Time Data in a Mobile Environment

Data broadcast is an important data dissemination approach in mobile environment. On broadcast channel,scalability and efficiency of data transmission are satisfied. In a mobile environment, there exists a kind of real-time database application in which both the transactions and data can have their timing constraints and priorities of different levels.In order to meet the requirement of real-time data disseminaring and retrieving, a broadcast scheduling strategy HPF-ED F (Highest Priority First with Earlier Deadline and Frequency) is proposed under the BoD (Broadcast on Demand) model. Using the strategy, data items are scheduled according to their priority the transaction imposed on them or system set for them. The strategy also considers other characteristics of data items such as deadline and popularity of data. The extensive simulation experiments have been conducted to evaluate the performance of the proposed algorithm. Results show that it can achieve excellent performance compared with existing strategies.     

Integrated Robust Design Method Based on Maximum Tolerance Region

Traditionally,parameter design is carried out prior to tolerance design. However, this two-step design strategy cannot guarantee optimal robustness for products＇ quality. The proposed integrated robust design method determined the optimal parameter and tolerance simultaneously by calculating the maximum tolerance region,thereby improving the quality of products. In addition,the proposed method did not need uncertainty analysis to obtain the maximum tolerance region,so that the calculation cost could be decreased. And the method avoided the difficulty of gaining costtolerance function as maximum tolerance region represented both demand of cost and robust. Finally,an amplifier circuit case was conducted for verification purpose. Based on the results, the proposed approach could provide robust solution with optimal maximum tolerance region.     

Oscillation Test Strategy for Analog Filters by Monitoring Output Voltage and Supply Current

A test strategy for analog filters was proposed. The output voltage and supply current of the circuit were monitored when using the oscillation test technique. The frequency, average value, maximum value and amplitude of both output voltage and supply current were taken as test parameters. Tolerance bands of test parameters were analyzed. Fault detectabilities of test parameters were compared and combined, and optimal parameter sets were derived. Experimental results show that both the output voltage and supply current give significant contribution to fault detection. Considering catastrophic, single and double parametric faults, the fault coverage in testing the benchmark circuit can be raised from 90.6% for traditional voltageonly oscillation test strategy to 97.2% by monitoring both output voltage and current parameters.     

Fuzzy Economic Order Quantity Inventory Models Without Backordering

In economic order quantity models without backordering, both the stock cost of each unit quantity and the order cost of each cycle are characterized as independent fuzzy variables rather than fuzzy numbers as in previous studies. Based on an expected value criterion or a credibility criterion, a fuzzy expected value model and a fuzzy dependent chance programming (DCP) model are constructed. The purpose of the fuzzy expected value model is to find the optimal order quantity such that the fuzzy expected value of the total cost is minimal. The fuzzy DCP model is used to find the optimal order quantity for maximizing the credibility of an event such that the total cost in the planning periods does not exceed a certain budget level. Fuzzy simulations are designed to calculate the expected value of the fuzzy objective function and the credibility of each fuzzy event. A particle swarm optimization (PSO) algorithm based on a fuzzy simulation is designed, by integrating the fuzzy simulation and the PSO algorithm. Finally, a numerical example is given to illustrate the feasibility and validity of the proposed algorithm.     

Autonomous control of multi-fingered hand

This paper describes a novel autonomous control strategy of multi-fingered hand based on a modular control system of dexterous manipulation. A simple proportional-integral-derivative(PID) position control with friction compensation, which requires few friction parameters, is used to realize accurate and smooth trajectory tracking in pregrasp phase. In grasp and manipulation phases, an event-driven switcher is adopted to determine the switching between unconstrained position control and constrained torque control, and an improved explicit integral force control strategy is implemented to realize simultaneously stable contact transition and accurate force tracking. Experimental results have verified the effectiveness of the proposed autonomous control strategy of multi-fingered hand.     

Design of Robust Controller and Force Observer for PM Linear Synchronous Motors

The advantages of permanent magnet linear synchronous motors （PMLSM） include high speed and good motion precision compared with rotary motors. However, PMLSM are sensitive to uncertainties such as the parameter perturbations and end effect etc. A new nonlinear robust scheme of PMLSM is proposed to overcome this trouble. First, a quasi-linearized and deeoupled model with uncertainties is derived from the mathematical model of PMLSM by using the conception of feedback linearization. Then a fixed-boundary-layer sliding mode controller using the m sat function is designed to guarantee the robustness. Design of a force observer is given to estimate the load force unknown in the new model. Finally, the validity of the proposed strategy compared with the conventional PID control scheme is proved by the DSpobased experimental results.     

Optimal Dispatching of Large—scale Water Supply System

This paper deals with the use of optimal control techniques in large-scale water distribution networks. According to the network characteristics and actual state of the water supply system in China, the implicit model, which may be solved by utilizing the hierarchical optimization method,is established. In special, based on the analyses of the water supply system containing variable-speed pumps, a software tool has been developed successfully. The application of this model to the city of Shenyang (China) is compared to experiential strategy. The results of this study show that the developed model is a very promising optimization method to control the large-scale water sup-ply systems.     

Strategic Robust Mixed Model Assembly Line Balancing Based on Scenario Planning

Assembly line balancing involves assigning a series of task elements to uniform sequential stations with certain restrictions. Decision makers often discover that a task assignment which is optimal with respect to a deterministic or stochastic/fuzzy model yields quite poor performance in reality. In real environments, assembly line balancing robustness is a more appropriate decision selection guide. A robust model based on the α worst case scenario is developed to compensate for the drawbacks of traditional robust criteria. A robust genetic algorithm is used to solve the problem. Comprehensive computational experiments to study the effect of the solution procedure show that the model generates more flexible robust solutions. Careful tuning the value of α allows the decision maker to balance robustness and conservativeness of as- sembly line task element assignments.     

Hybrid Dynamic Modeling and Control of Molten Carbonate Fuel Cell Stack Shutdown

A hybrid automaton modeling approach that incorporates state space partitioning, phase dynamic modeling and control law synthesis by control strategy is utilized to develop a hybrid automaton model of molten carbonate fuel cell （MCFC） stack shutdown. The shutdown operation is divided into several phases and their boundaries are decided according to a control strategy, which is a set of specifications about the dynamics of MCFC stack during shutdown. According to the control strategy, the specification of increasing stack temperature is satisfied in a phase that can be modeled accurately. The model for phase that has complex dynamic is approximated. The duration of this kind of phase is decreased to minimize the error caused by model approximation.     

Convex Duality in Portfolio Optimization under Constraints and with Higher Interest Rate for Borrowing

We study the stochastic control problem of maximizing expected utility from terminal wealth and/or consumption, when the portfolio is constrained to take values in a given closed, convex subset of Rd, and in the presence of a higher interest rate for borrowing. The setting is that of a continuous-time, Ito process model for the underlying asset prices. The solution of the unconstrained problem is given. In addition to the original constrained optimization problem, a so-called combined dual problem is introduced. Finally, the existence question of optimal processes for both the dual and the primal problem is settled.     

Empirical measurement of the software testing and reliability

The meanings of parameters of software reliability models are investigated in terms of the process of the software testing and in terms of other measurements of software. Based on the investigation, the empirical estimation of the parameters is addressed. On one hand, these empirical estimates are also measurements of the software, which can beused to control and to optimize the process of the software development. On the other hand, by treating these empirical estimates as Bayes priors, software reliability models are ex-tended such that the engineers‘ experience can be integrated into and hence to improve the models.     

Effect of Deformation between Stands on the Strip Shape in Hot Rolling

A theoretical model about the secondary deformation is developed by the combination of analytical and experimental approaches. A system simulation based on the model is completed to predict the strip profile after the interstand deformation. On the other hand, comprehensive experiments and quantitative comparisons are made to calibrate the model for a wide variety of products. As a result, the correction of the model has been verified by the actual rolling data from production mill and it is helpful to improve the strip shape control.     

膏体增稠剂底流固相分数和屈服应力的循环变化行为

The trouble-free and efficient operation of paste thickeners requires an optimal design and the cooperation of each component. When underflow discharging is suspended, alleviating the vast torque that the remaining solids within the thickeners may place on rakes mainly lies in the circulation unit. The mechanism of this unit was analyzed, and a mathematical model was developed to describe the changes in underflow solid content and yield stress. The key parameters of the circulation unit, namely, the height and flow rate, were varied to test its performance in the experiments with a self-designed laboratorial thickening system. Results show that the circulation unit is valid in reducing underflow solid fraction and yield stress to a reasonable extent, and the model could be used to describe its efficiency at different heights and flow rates. A suitable design and application of the circulation unit contributes to a cost-effective operation of paste thickeners.