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.
Investment risk evaluation of hi-tech projects based on projection pursuit model
The hi-tech projects' investment is characterized with high risk and huge profit, so a scientific and accurate risk evaluation model is of importance to projects' investment. Based on the analysis of the indicators influencing the investment risk of hi-tech projects, some typical indicators were selected. Firstly, according to the quantitative assessment results from the experts, some samples were used to establish the projection pursuit model for comprehensive evaluating the investment risk of the hi-tech projects. Then, the model without samples was constructed to avoid the impact of training samples. The optimal projection vector was optimized by genetic algorithm, so the above problem was transformed into one dimension problem. Compared with the conventional assessment methods such as AHP (Analytic Hierarchy Processing) and ANN (artificial neural net), the model can effectively overcome the faults of the subjective feature from the AHP and unsatisfied generalization ability from ANN. Additionally, under no training samples, the model can also sort well, so the model has wide applicability.
Time and Heritage
As a historian studying what can be considered a form of intellectual history, I have slowly come to adopt Michel de Certeau’s observation made at the end of the 1980s that ‘objectifying the past, for the last three centuries, has undoubtedly left unconsidered time within a discipline that has continued to use it as a taxonomic instrument’. To a certain degree, time has become commonplace for the historian who has preserved or instrumentalized it. It is not considered because it is inconceivable, but because we do not think of it or, more simply, we do not think about it. As a historian attempting to pay attention to the time I’m living in, I have thus, like many others, noticed the swift development of the category of the present until it has become obvious that the present is omnipresent. This is what I refer to here as ‘presentism’.