Table of contents
First, the abstract must be carefully written
Second, the typesetting of the paper must be beautiful
Third, model assumptions must be taken seriously
●Significance of model assumptions
4. Problem Analysis Recommended Use Flowchart
5. It is recommended to use the improved or optimized model
Sixth, it is recommended to add a model checking module
First, the abstract must be carefully written
●In the selection of mathematical modeling papers, there are usually two stages: preliminary review and final review. The preliminary review is mainly for the judges to judge whether they can enter the final review by looking at the abstracts of the participating students. Generally, the time required for this process is 5- 10 minutes; papers that enter the final review stage have a high probability of winning prizes (for example, at least 80% of the probability of winning a prize in the final review of the US competition), and papers that do not enter the final review can only be awarded an excellent award. A good abstract should
include "Tiger head", "pork belly" and "leopard tail", clear structure, rigorous logic, rich content, concise language
The abstract must not exceed one page, usually half a page or 2/3 of a page
Second, the typesetting of the paper must be beautiful
The thesis is the only material presented to the judges by the team members, so the quality of the thesis will directly affect the final award; the
typesetting of the thesis refers to the process of beautifying the thesis according to the prescribed standard format; a well-typed thesis It will make the eyes of the judges
shine, and it will be more intuitive when reviewing, and it will be easier to get good grades. Generally speaking, it is recommended to use LaTeX typesetting software for English papers
, and for non-English papers, you need to edit according to the template. Use mathtype for formulas, and the charts should be beautiful
Third, model assumptions must be taken seriously
●Many small partners do not pay much attention to the assumptions of the model when writing, but the assumptions of the model are directly mentioned in the paper review criteria, and it is also a place that the judges of the national competition pay more attention to. Model assumption is an essential link before the model is established, and the model assumption directly relates to the success or failure of modeling;
●Significance of model assumptions
●(Quoted from "Guidelines for College Students Mathematical Contest in Modeling" Xiao Huayong)
●1. Focus on the main factors of the problem and ignore the secondary factors;
●2. Simplify the problem we want to solve and make the model more reasonable;
●3. Model assumptions Importance—the success or failure of relational modeling and its advantages and disadvantages.
Don’t write too many model assumptions. Generally, you can write 5^10 items.
4. Problem Analysis Recommended Use Flowchart
●Problem analysis can let the judges intuitively understand the author's modeling intention and main problem-solving ideas, so it must be taken seriously; in order to facilitate the review of the judges, it is recommended to add a flow chart in the problem analysis part. The flow chart can be used with VISI0 software or WPS. The flow chart creation
module also needs to be described in text below the flow chart, and it is forbidden to provide only a flow chart without a corresponding text description.
5. It is recommended to use the improved or optimized model
The establishment of models is one of the most important modules for thesis review. Generally speaking, each contestant mainly chooses an existing model to answer
questions students can create a brand new model within three days of the competition. Model. Therefore, the quality of model selection will also directly affect
the results of the review. It is recommended that you give priority to combined models or improved models in model selection, such as
●Comprehensive evaluation model based on AHP-entropy weight method (evaluation of competition questions, more accurate weight determination)
●Comprehensive prediction model based on gray-BP neural network (forecasting competition questions, small sample prediction)
●BP neural network optimization algorithm based on genetic algorithm (both evaluation and prediction classes can be applied, with higher precision)
●Forecasting model based on wavelet transform-neural network (forecasting competition questions, large sample prediction)
Combining or improving the optimized model can maximize the advantages of the original algorithm and compensate for its disadvantages
Sixth, it is recommended to add a model checking module
●Model checking is different from the evaluation of the advantages and disadvantages of the model. Model checking mainly includes two modules: error analysis and sensitivity analysis. Error analysis can verify the correctness of the model, and sensitivity analysis mainly verifies the universality of the model. Adding model checking can give the judges a more comprehensive understanding of the correctness of the established model, and more recognition of the results achieved by the modeling.
●Error analysis is generally applicable to prediction topics, to judge or analyze whether the calculation results of the model are accurate
●Sensitivity analysis is generally applicable to certain fixed parameters in the model, mainly to determine whether the model is applicable to more scenarios