Two-level Fuzzy Method for Evaluation of the Supplier''s System in Automobile Industry

This paper analyses the importance of the suppliers' evaluation in automobile industry. Based the conception of system, all the corporations' suppliers are regarded as an integer. On condition that the corporation has evaluated the single supplier's performance, the model for evaluate the performance of supplier's system is established, the performance by two-level fuzzy method is evaluated. Finally, the method sets with an example as the illustration.     

A Furry Synthetic Evaluation Model in Capital Structure Decisions

This paper analyzes the limitation of regarding the weighted average cost of capital as the optimal standard of capital structure in the traditional capital structure decision-making method, and analyzes the view that the capital structure is affected by many factors,such as earning power, long-term stability of operating, the attitudes held by owners and operators, the attitudes held by creditors and financial risk. With the example, the affecting factors of the capital structure is analyzed. The fuzzy synthetic evaluation model are employed to decision-making of the capital structure. This article also shows the key point of its application.     

Using BP Neural Network to Determine the Membership Function of the Input of Fuzzy Control System

While design the fuzzy controller, it is very important to determine the membership function of fuzzy variables.The data can be broadly classified as fuzzy sets by using the classification property of the BP neural network. The author selects a BP neural network with one hide layer and uses S function to the input and hide layer, and linear function to the output layer.Advanced BP algorithm isused to train the BP neural network in the environment of MATLAB . The nearer to the target values is the better the last output is.With the trained BP network , the membership values of the inputs can be got ten. This method has high rate and low error.     

Improved Genetic Algorithm and Its Application in TSP

Crossover operator and mutation operator are basic operators of genetic algorithm , which play important roles in improving the quality of the solution populations. We propose the improved crossover operator , mutation operator and rotation operator on the basis of good point set with the traveling salesman problem , the experimental result shows these operators are very efficient.     

Application of Multivector Algebra in Real Space-time to Set Pair Analysis

The contact number is an important mathematical tool of systems theory and methods in the monograph Set Pair Analysis and Its Preliminary Applications written by Zhao Keqin for unitizedly processing the uncertainties due to the fuzzy, stochastic, intermediate and information uncomplete about something. The article applies the multivector algebra which is isomorphism with the real Dirac algebra in the real space-time adopted by Venzo de Sabbata to the contact number of set pair analysis, and consequently, the contact number is generalized correspondingly.     

Application of Attribute Reduct Method in Spatial Load Forecasting

Spatial load forecasting is a process distributing the total forecasted load to all partitioned area, and involving more spatial information and more factor influencing application of the future small area, which need a great deal of memory space and longer operation time. Rough set is new method of data analysis. It need not be provided with any advanced information except data set. But attribute reduct is its main algorithms. Division matrix approach on rough set used to reduce the attribute related to land - use decision in order to remove redundancy attribute and then the rules of small area land - use decision is distilled. The method obtained better effect and enhanced the total load forecasting efficiency.     

New Algorithms for Data Discretization Based on Rough Set Theory

The discretization of real values is always one of the key problems to be solved in the domain of machine learning for its great contribution to speeding up the followed learning algorithms, cutting down the real demand of algorithms on running space and time, and improving the clustering capability of the ultimate learning results. The basic characteristics and framework of discretization approaches based on rough set model are analyzed at first, then the different measurements of the importance of candidate cuts are discussed and researched. Two new heuristic algorithms are put forward to finally select the useful cuts from a candidate set. The selected cuts of the two algorithms will adequately maintain the discernible relation of information systems for their full considering the specialty of rough set, which perfectly embodies the advantages of this theory. Moreover, excellent discretization results may be expected through these heuristic algorithms.     

Determination to Weighting Coefficient of Combination Forecast Based on Rough Set Theory

The problem of determination to weighting coefficient is a key and difficulty for combination forecast. A method of determining weighting coefficient based on rough set theory is showed in this paper. Determining weighting coefficient is translated into estimating significance of attributes among rough set. A relation data model about combination forecast is established. Knowledge systems are built through making attribute value into eigenvalue. Under data moving, the weighting coefficients of a combination forecast model are computed by analyzing the dependence and significance of forecasting method for the predicted object. The proposed approach overcomes the subjectivity of traditional determination to weighting coefficient, avoids computing linear or nonlinear extremum problem and makes combination forecast more objective. The validity of the proposed approach is verified with a case.