Cloud Transform Algorithm Based Model for Hydrological Variable Frequency Analysis

Hydrological variable frequency analysis is a fundamental task for water resource management and water conservancy project design. Given the deficiencies of higher distribution features for the upper tail section of hydrological variable frequency curves and the corresponding safer resulting design of water conservancy projects utilizing the empirical frequency formula and Pearson type III function-based curve fitting method, the normal cloud transform algorithm-based model for hydrological variable frequency analysis was proposed through estimation of the sample empirical frequency by the normal cloud transform algorithm, and determining the cumulative probability distribution curve by overlapping calculation of multiple conceptual cloud distribution patterns, which is also the primary innovation of the paper. Its application result in northern Anhui province, China indicated that the varying trend of the cumulative probability distribution curve of annual precipitation derived from the proposed approach was basically consistent with the result obtained through the traditional empirical frequency formula. Furthermore, the upper tail section of the annual precipitation frequency curve derived from the cloud transform algorithm varied below the calculation result utilizing the traditional empirical frequency formula, which indicated that the annual precipitation frequency calculation result utilizing the cloud transform algorithm was more optimal compared to the results obtained by the traditional empirical frequency formula. Therefore, the proposed cloud transform algorithm-based model was reliable and effective for hydrological variable frequency analysis, which can be further applied in the related research field of hydrological process analysis.

Selection of Fuzzy Controller Membership Functions Based on Adaptive Gaussian Cloud Transform

Aiming at the boundedness of existing methods of selecting membership functions, an adaptive Gaussian cloud transform algorithm which is guided by the threshold values of hybridization degree is proposed to construct concept hierarchy from original sample data, and then the number, shape and coverage area of membership functions can be derived from the distribution of Gaussian cloud. To test and verify the effectiveness of membership function that is extracted based on adaptive Gaussian cloud transform algorithm, a six-degree-of freedom model of unmanned aerial vehicles(UAV) is constructed, and a fuzzy controller of pitching angle is established with the platform of Simulink. The simulation results show that the fuzzy controller which includes membership functions derived from the distribution of Gaussian cloud transform can achieve perfect control performance of pitching angle and meanwhile obtain good dynamic response characteristics.

A Model of E-Commerce Credit Evaluation Based on Cloud Theory

Traditional evaluation methods do not consider that e-commerce credit evaluation is inherently uncertain and ambiguity .This kind of method makes the evaluation results unnatural. In this paper, a model of e-commerce credit evaluation based on cloud theory is presented. It can transfer quantitative data to qualitative data by cloud transform and cloud maker and matches the index for rules by cloud reasoning engine. If this model is used to explain the result of evaluation, the result could be easier to understand.

Cloud Relation Model and its Application

Based on Cloud Transform and Noclassical Relation Database Theory,the Paper Define Cloud Relation Model and Discuss the Significance of Cloud Relation Model.At the same Time ,based on Cloud Relation Model,the Paper Ameliorate the Way of Qualitative Estimate about Student Scores，conquer the Subjective Factor and Presents the Objective Way of Qualitative Estimate about Students .The Examples Prove the Way Is Feasible.