Design of Improved Algorithm and Model for Multi-Constrained Fuzzy Predictive Analysis

Abstract

Complex system engineering often has high fuzziness and multiple constraints. In this case, it is difficult to achieve consistent results through predictive analysis. To solve the problem, this paper explores the key techniques and methods for predictive analysis on complex systems, and puts forward an improved strategy for multi-constrained fuzzy predictive analysis. The author explained the normalization, weighting, granularity setting and classic domain of the attributes of multiple constraints, introduced the calculation of the fuzzy distance and fuzzy closeness for the attributes of multiple constraints, and detailed the realization of our algorithm and model multi-constrained fuzzy predictive analysis. The effectiveness and feasibility of our algorithm and model were demonstrated through comparison with relevant data in the literature. The results show that the results of our approach agree with those of the literature. The proposed algorithm and model provide a good theoretical basis to predictive analysis of complex system engineering, especially that with multiple fuzzy constraints.

Training Strategies for Practical Ability of College Students Majoring in Computer-Aided Design

“…But due to different analysis perspectives, they have different understanding and focus on training strategies and models of college students' practical ability, which limits their research on the training and requires further systematic discussion. In view of the above, this paper further analyzes the training strategy of practical ability of college students majoring in CAD of mechanical engineering, and proposes an evaluation model based on entropy weight [12][13] and fuzzy theory [14][15]. It consists of 6 parts in total.…”