提高民族地区小学生的计算能力初探

针对民族地区小学生源特点,学生基础参差不齐,个体差异较大,部分学生接受知识较慢,计算能力较为薄弱。通过调查分析,找出学生计算错误的原因,并对症下药,提高民族地区小学生的计算能力,从而提高教学效率和学习效果。

Multiple vehicle routing problem integrated reverse logistics with fuzzy reverse demands

A new type of vehicle routing problem （VRP）, multiple vehicle routing problem integrated reverse logistics （MVRPRL）, is studied. In this problem, there is delivery or pickup （or both） and uncertain features in the demands of the clients. The deliveries of every client as uncertain parameters are expressed as triangular fuzzy numbers. In order to describe MVRPRL, a multi-objective fuzzy programming model with credibility measure theory is constructed. Then the simulationbased tabu search algorithm combining inter-route and intra-route neighborhoods and embedded restarts are designed to solve it. Computational results show that the tabu search algorithm developed is superior to sweep algorithms and that compared with handling each on separate routes, the transportation costs can be reduced by 43% through combining pickups with deliveries.

Improvement of Fuzzy Inference Mechanism in FuzzyJ Toolkit

By applying the aggregation operator γ-operator and introducing a new method for global data contribution, the problems of information loss and the decrease of running efficiency in FuzzyJ Toolkit, an expert system shell, can be effectively solved. The example shows that the approach can overcome imprecision of max-operator and min-operator used during the process of fuzzy reasoning. Therefore, the information accuracy and the system performance can be effectively improved, which promotes the usability of FuzzyJ Toolkit.

Composition ship collision risk based on fuzzy theory

Despite of modern navigation devices, there are problems in navigation of vessels in waterways due to the geographical structures, disturbances in water, dynamic nature, and heavily environmental influenced sea traffic. Even though all vessels are equipped with modern navigation devices, the accidents are reported caused by various reasons and mainly by human factor according to investigation. We propose an effective and efficient composition collision risk calculation method for finding the collision probability and avoiding the collision between ships in possible collision situations. The proposed composition collision risk calculation method at ship's position using combination of fuzzy and fuzzy comprehensive evaluation methods. The algorithm is straightforward to implement and is shown to be effective in automatic ship handling for ships involved in complex navigation situations. Experiments are carried out with indigenous data and the results show the effectiveness of the proposed approach.

DETERMINGING BPA UNDER UNCERTAINTY ENVIRONMENTS AND ITS APPLICATION IN DATA FUSION

Dempster-Shafer (DS) theory of evidence has been widely used in many data fusion ap- plication systems. However, how to determine basic probability assignment, which is the main and the first step in evidence theory, is still an open issue. In this paper, a new method to obtain Basic Probability Assignment (BPA) is proposed based on the similarity measure between generalized fuzzy numbers. In the proposed method, species model can be constructed by determination of the min, average and max value to construct a fuzzy number. Then, a new Radius Of Gravity (ROG) method to determine the similarity measure between generalized fuzzy numbers is used to calculate the BPA functions of each instance. Finally, the efficiency of the proposed method is illustrated by the classi- fication of Iris data.

A Fuzzy-based Adaptive Genetic Algorithm and Its Case Study in Chemical Engineering

Considering that the performance of a genetic algorithm （GA） is affected by many factors and their rela-tionships are complex and hard to be described,a novel fuzzy-based adaptive genetic algorithm （FAGA） combined a new artificial immune system with fuzzy system theory is proposed due to the fact fuzzy theory can describe high complex problems.In FAGA,immune theory is used to improve the performance of selection operation.And,crossover probability and mutation probability are adjusted dynamically by fuzzy inferences,which are developed according to the heuristic fuzzy relationship between algorithm performances and control parameters.The experi-ments show that FAGA can efficiently overcome shortcomings of GA,i.e.,premature and slow,and obtain better results than two typical fuzzy GAs.Finally,FAGA was used for the parameters estimation of reaction kinetics model and the satisfactory result was obtained.

Image Distortion Metric Based on Total Bounded Variation

Image definition measurement plays an important role in various image processing applications.And a reliable objective image definition metrics is critical for evaluating the definition of the restored image.In this paper,a novel image distortion metric based on minimal Total Bounded Variation(TBV) is presented.It is clarified that when the restored image approximates to the original clear image,the smaller the TBV is,the better the definition of the restored image is.Furthermore,the difference between the restored image and the original clear image is the smallest when the TBV is minimum.In numerical results,the TBV of the original clear image,blur image and restored image are presented and compared,and the results demonstrate the validity of the distortion metric proposed.

Optimal fuzzy PID controller with adjustable factors based on flexible polyhedron search algorithm

A new kind of optimal fuzzy PID controller is proposed, which contains two parts. One is an on line fuzzy inference system, and the other is a conventional PID controller. In the fuzzy inference system, three adjustable factors x p, x i , and x d are introduced. Their functions are to further modify and optimize the result of the fuzzy inference so as to make the controller have the optimal control effect on a given object. The optimal values of these adjustable factors are determined based on the ITAE criterion and the Nelder and Mead′s flexible polyhedron search algorithm. This optimal fuzzy PID controller has been used to control the executive motor of the intelligent artificial leg designed by the authors. The result of computer simulation indicates that this controller is very effective and can be widely used to control different kinds of objects and processes.

Some Insights in Novel Risk Modeling of Liquefied Natural Gas Carrier Maintenance Operations

This study discusses the analysis of various modeling approaches such as genetic algorithms, fuzzy logic and evidential reasoning, and maintenance techniques applicable to the liquefied natural gas （LNG） carrier operations in the maritime environment. The usefulness of these algorithms in the LNG carrier industry in the areas of risk assessment and maintenance modeling as a standalone or hybrid algorithm are identified. This is evidenced with illustrative case studies.

A New Fuzzy Clustering-Ranking Algorithm and Its Application in Process Alarm Management

Overmany alarms of modern chemical process give the operators many difficulties to decision and diag- nosis. In order to ensure safe production and process operating, management and optimization of alarm information are challenge work that must be confronted. A new process alarm management method based on fuzzy clustering- ranking algorithm is proposed. The fuzzy clustering algorithm is used to cluster rationally the process variables, and difference driving decision algorithm ranks different clusters and process parameters in every cluster. The alarm signal of higher rank is handled preferentially to manage effectively alarms and avoid blind operation. The validity of proposed algorithm and solution is verified by the practical application of ethylene cracking furnace system. It is an effective and dependable alarm management method to improve operating safety in industrial process.

Optimal design of structural parameters for shield cutterhead based on fuzzy mathematics and multi-objective genetic algorithm

In order to improve the strength and stiffness of shield cutterhead, the method of fuzzy mathematics theory in combination with the finite element analysis is adopted. An optimal design model of structural parameters for shield cutterhead is formulated,based on the complex engineering technical requirements. In the model, as the objective function of the model is a composite function of the strength and stiffness, the response surface method is applied to formulate the approximate function of objective function in order to reduce the solution scale of optimal problem. A multi-objective genetic algorithm is used to solve the cutterhead structure design problem and the change rule of the stress-strain with various structural parameters as well as their optimal values were researched under specific geological conditions. The results show that compared with original cutterhead structure scheme, the obtained optimal scheme of the cutterhead structure can greatly improve the strength and stiffness of the cutterhead, which can be seen from the reduction of its maximum equivalent stress by 21.2%, that of its maximum deformation by 0.75%, and that of its mass by 1.04%.

A NEURAL FUZZY INFERENCE SYSTEM

This paper proposes a new neural fuzzy inference system that mainly consists of four parts. The first part is about how to use neural network to express the relation within a fuzzy rule. The second part is the simplification of the first part, and experiments show that these simplifications work. On the contrary to the second part, the third part is the enhancement of the first part and it can be used when the first part cannot work very well in the fuzzy inference algorithm, which would be introduced in the fourth part. Finally, the fourth part "neural fuzzy inference algorithm" is been introduced. It can inference the new membership function of the output based on previous fuzzy rules. The accuracy of the fuzzy inference algorithm is dependent on neural network generalization ability. Even if the generalization ability of the neural network we used is good, we still get inaccurate results since the new coming rule may not be related to any of the previous rules. Experiments show this algorithm is successful in situations which satisfy these conditions.

Objective function of cost in optimal tolerance allocation

An objective function model is proposed for cost in optimizing and allocating tolerance with consideration of manufacturing conditions. With the fuzzy comprehensive evaluation method,a manufacturing difficulty coefficient is derived,which takes into account of several factors affecting the manufacturing cost,including the forming means of the blank,size,machining surface features,operator’s skills and machinability of materials. The coefficient is then converted into a weight factor used in the inversed square model representing the relationship between the cost and tolerance,and,hence,an objective function for cost is established in optimizing and allocating tolerance. The higher is the manufacturing difficulty coefficient,the higher is the relative manufacturing cost and the higher is the weight factor of the tolerance allocation,which indicates the increase of the tolerance’s effects on the total manufacturing cost and,therefore,a larger tolerance should be allocated. The computer-aided tolerance allocation utilizing this model makes it more convenient,accurate and practicable.

Prediction of rock mass rating using fuzzy logic and multi-variable RMR regression model

Rock mass rating system （RMR） is based on the six parameters which was defined by Bieniawski （1989） [1]. Experts frequently relate joint and discontinuities and ground water conditions in linguistic terms with rough calculation. As a result, there is a sharp transition between two modules which create doubts. So, in this paper the proposed weights technique was applied for linguistic criteria. Then by using the fuzzy inference system and the multi-variable regression analysis, the accurate RMR is predicted. Before the performing of regression analysis, sensitivity analysis was applied for each of Bieniawski parameters. In this process, the best function was selected among linear, logarithmic, exponential and inverse func- tions and finally it was applied in the regression analysis for construction of a predictive equation. From the constructed regression equation the relative importance of the input parameters can also be observed. It should be noted that joint condition was identified as the most important effective parameter upon RMR. Finally, fuzzy and regression models were validated with the test datasets and it was found that the fuzzy model predicts more accurately RMR than reression models.

A MODEL OF FUZZY CALCULATION OF THE CONSTUCTION COST

An overview of the delelopment of approaches to construction cost and price forcasting since the 1950’s is given. First, second and third generation models can be identified, but they all have shortcomings. This paper puts forward a new model, fuzzy calculation model, based on lots of data of the finished proiects. Through actual application, it is proved that the model is accurate and quick in calcalation of construction.

Advancing Fog Effect on VRML and X3D Using Local Fog Density

In this paper an improved fog effect algorithm in VRML and X3 D is presented with respect to expressing density. The fundamental idea in the approach is to adapt local fog density having influence on Iocal regions with various grades of fog density whereas existing VRML and X3 D only make use of global fog effect. Several filters for making different fog density are presented along with experiments showing the correctness of the proposed method.

Generating Fuzzy Rule-based Systems from Examples Based on Robust Support Vector Machine

This paper firstly proposes a new support vector machine regression (SVR) with a robust loss function, and designs a gradient based algorithm for implementation of the SVR, then uses the SVR to extract fuzzy rules and designs fuzzy rule-based system. Simulations show that fuzzy rule-based system technique based on robust SVR achieves superior performance to the conventional fuzzy inference method, the proposed method provides satisfactory performance with excellent approximation and generalization property than the existing algorithm.

A New Venture Analysis Method and Its Application

The new venture analysis is the foundation of venture development. In this paper, 14 venture prototypes are proposed based on the attributes of venture.Then, a new venture analysis method is discussed by the way of matching the new venture with the corresponding prototype. Considering the fuzziness of human subjective grading, the L-R fuzzy numbers are used to express the variables and corresponding fuzzy algorithm are applied in analysis. At the end, an application example is applied to indicate the effectiveness of the method.

Tracking Target Identification Model Based on Multiple Algorithms

In view of current situation of bad data synchronization, image blurring and tracking station stability in tracking target identification, a kind of tracking target identification model based on multiple algorithms was put forward, firstly establishing the image degradation model, using the wavelet algorithm for image preprocessing, doing image edge segmentation by using Robert algorithm after pretreatment, then using the maximum variance threshold method for image threshold segmentation, then extracting target features from the segmented image, and finally using the ABS algorithm to finish target tracking. Experiments proved the proposed model practical and effective.

Modeling of shear wave velocity in limestone by soft computing methods

The main purpose of current study is development of an intelligent model for estimation of shear wave velocity in limestone. Shear wave velocity is one of the most important rock dynamic parameters. Because rocks have complicated structure, direct determination of this parameter takes time, spends expenditure and requires accuracy. On the other hand, there are no precise equations for indirect determination of it; most of them are empirical. By using data sets of several dams of Iran and neuro-genetic, adaptive neuro-fuzzy inference system （ANFIS）, and gene expression programming （GEP） methods, models are rendered for prediction of shear wave velocity in limestone. Totally, 516 sets of data has been used for modeling. From these data sets, 413 ones have been utilized for building the intelligent model, and 103 have been used for their performance evaluation. Compressional wave velocity （Vp）, density （7） and porosity （.n）, were considered as input parameters. Respectively, the amount of R for neuro-genetic and ANFIS networks was 0.959 and 0.963. In addition, by using GEP, three equations are obtained; the best of them has 0.958R. ANFIS shows the best prediction results, whereas GEP indicates proper equations. Because these equations have accuracy, they could be used for prediction of shear wave velocity for limestone in the future.