The Mathematic Method of Membership Conversion from Degree of Coordinates to Degree of Points
In any multi-indexes uncertainty decisions, if choosing membership function to describe the uncertainty of every space-index value about some status, it must meet membership degree conversion, which must solve the conversion from membership degree of coordinates to membership degree of points. But the present methods affect the credibility of result of decision because of the redundant data. So, by mining knowledge information about points’ classification hidden in membership degree of every coordinates, affirming the relation between points’ classification and membership degree of coordinates; eliminating the redundant data in coordinates’ membership for points’ classification by defining distinguishable weight; constructing the mathematic method of conversion from membership degree of coordinates to membership degree of points without the interference of redundant data, providing method basis for solving key computation in multi-indexes uncertainty decisions.
A Method of Comprehensive Evaluation with Subjective and Objective Information Based on Evidential Reasoning and Rough Set Theory
A comprehensive evaluation method is set forth based on evidential reasoning and rough set theory, which is used to deal with multi-level, multi-attribute evaluation problem. This method can utilize subjective information and objective information effectively. Evidential reasoning is used to deal with the information of subjective judgement. At the same time rough set theory is used to obtain objective evaluation knowledge from used data. So the results which conform with engineering practice would be gotten by means of combining them during evaluation. The model and algorithm are introduced. At last, it proves the method is rational and reasonable through the example of evaluating design selection of a gearbox.
Comprehensive Evaluation Model for the Investing Risk of Using Artificial Neural Networks in the Hi-tech Projects
The hi-tech projects′ investment is characterized by high risks and huge profits, so an accurate, comprehensive and scientific risk evaluation model is of great important to projects′ investment. Based on the analysis of the indicators influencing the investment risk of hi-tech projects, the artificial neural network-based model (ANN-Model) for comprehensive evaluating the investing risk of the hi-tech projects was established using the efficient samples, produced according to evaluation standard for each evaluation indicator and the random-distribution theory, for learning, verifying and testing the neural network model.Comparison with the AHP (Analytic Hierarchy Processing), the ANN-Model possesses the accurate, objective and accuracy. the actual case study shown that the ANN-Model can well be applied to the comprehensive evaluation for the investment risk of the hi-tech projects.