With the concept of sustainable development, enterprises are facing severe challenges in ecological protection and economic development. Approaches to improving effectiveness of the coordinated development strategy must continue to evolve to address uncertainty and hazards that may be encountered in the future. We propose a coordinated development strategy model based on the combination of soft fuzzy and rough set theory and construct its prediction model. For the multistrategy dataset in the paper, parameter for each kind shall be selected through converting the multistrategy data into two prediction datasets. An algorithm transformed by SFRC shall be subject to weighted average for each parameter. Furthermore, we use training methods and soft fuzzy rough sets’ learning algorithm to calculate, and the evaluating indicator rough set is constructed with a three-tier model structure. After the final rough set training is completed, test results show that the rough set model which has a higher rating accuracy builds a better completed business performance evaluation. By comparing the prediction effect, both SVM algorithm and multistrategy prediction model for the soft fuzzy rough set in the paper can realize effective prediction for the enterprise’s coordinated development strategy. Moreover, the prediction result obtained at the time of adopting boundary to get the expected value is superior to that of giving one fixed threshold. It shows that the prediction performance of the algorithm in the paper is more excellent and represents the advantage of the algorithm prediction performance at the time of adopting boundary to get the expected value. The model provides support methods to assist enterprise management in making more efficient and scientific decisions for enterprise’s coordinated development.

An American scientist Rachel Carson warned the world in the famous book “

Rough set theory [

Multiple-criteria decision-making methods have appeared in the last decade [

For the coordinated development strategy of enterprises, the growth curve model can be utilized for construction, and the growth curve model is one kind of curve to describe the growth process for creatures originally. It is found through observation that speed for the growth process of many things changes slowly and then gradually speeds up. After it reaches the quickest growth speed, it starts to slow down again. Finally, its growth speed is nearly approximate to the dead state to reach some extreme.

In case

This is one nonlinear ordinary differential equation. Supposing its initial value

This equation is the logistic curve equation, and

Grey system refers to a system with incomplete and inaccurate information to be used as grey prediction, and the GM (1, 1) model is the most frequently used. It is a model for the first-order grey differential equation of 1 variable. Variable set

Its corresponding differential model is

Through mathematical derivation, the calculation equation for the following pending parameter that needs to be calculated can be obtained:

And the sequence for generating the model is

Through the sequence of equation (^{−ak} item is for time

Thinking of selecting soft threshold in the soft margin, SVM is introduced into fuzzy rough set theory, and one kind of concept different from soft distance for the recent distance method of the original calculation sample is proposed.

Given one sample practice

One example of confirming soft distance is given in Figure _{1} is taken as one noise sample and is ignored, SD(_{2}. Therefore, one penalty item is needed to judge whether how many samples shall be ignored. In case one sample is ignored, _{j})_{j}), _{j}) is the maximum after punishing all ignored samples. This is about selection for parameter

On the basis of soft distances shown in Figure

Schematic diagram for soft distance.

Taking

Hu et al. designed one robust predictor on the basis of the lower approximation definition for the above soft fuzzy which can be used to solve single-strategy prediction [

Input: training sample set

Output: effect class

Step 1: calculate the effect number.

Step 2: for each test sample

For each kind class _{1}, _{2}, …, _{k}}), calculate the distance between

For the obtained candidate distance sequence, calculate the corresponding soft distance for kind class

It can be known from equations (

Corresponding kind strategy class

Step 3: repeat Step 2 until a kind strategy is obtained for each test sample.

The algorithm is described as follows:

It can be seen from Figure

RMSE change curve in the training process.

Taking one sample

For the multistrategy dataset in the paper, parameter for each kind shall be selected through converting the multistrategy data into two prediction datasets. BR method has different parameter values for different kinds, which can be seen from equation (

The equation to calculate parameter

Since the input level of the rough set is unavoidable, the number of input dimensions will increase exponentially which will inevitably increase the model’s large structural complexity and training and learning times. To solve this issue, reduce the impact of subjective factors on the evaluation results, and then adopt the evaluation method on the basis of the layered rough set, we firstly use rough set training simulation in the evaluating indicator and use rough set training simulation in the nonevaluating indicator. Then, we conduct rough set training on their results again, and finally, we evaluate the results of the enterprise. We set the evaluation result as

This paper uses training methods and soft fuzzy rough set learning algorithm to calculate, and the evaluating indicator rough set is built with a three-tier model structure. In the structure, the input layer node number is 15, the output layer node number is 1 (i.e., the level of the evaluating position), and the number of hidden layer nodes is 10 (in line with the Kolmogorov theorem). At this time, rough set training error is the smallest, and the training time is the shortest (see Figure

Mean square error of the training data curve.

After the final rough set training is completed, the test sample is put into the well-trained rough set for performance evaluation. Test results’ expected output in Figure

Time output and expected output of the model.

The coordinated development research object adopted in [

Prediction result for the multistrategy predictor.

Index | Algorithm | |
---|---|---|

BR | ||

Algorithm in this paper | SVM | |

Exact match | 0.6280 | 0.6060 |

Hamming loss | 0.0537 | 0.1006 |

Accuracy | 0.7957 | 0.7173 |

Precision | 0.9229 | 0.7756 |

Recall | 0.8154 | 0.7563 |

0.8475 | 0.7605 |

It can be seen from Table

It can be seen from Table

Influence result of threshold.

Index | Algorithm | |||||
---|---|---|---|---|---|---|

Algorithm in this paper | ||||||

90% | 92% | 94% | 96% | 98% | Mean | |

Exact match | 0.2990 | 0.3510 | 0.3870 | 0.3870 | 0.3090 | 0.6000 |

Hamming loss | 0.5128 | 0.4373 | 0.3588 | 0.2760 | 0.1988 | 0.1003 |

Accuracy | 0.4630 | 0.5141 | 0.5577 | 0.5842 | 0.5606 | 0.7143 |

Precision | 0.4811 | 0.5489 | 0.6234 | 0.7047 | 0.7702 | 0.7806 |

Recall | 0.9694 | 0.9392 | 0.8924 | 0.8174 | 0.6827 | 0.7507 |

0.5589 | 0.6033 | 0.6425 | 0.6704 | 0.6574 | 0.7583 |

In order to cope with the inherent ambiguity and complexity of actual decision-making problems, fuzzy set theory and rough set theory, as two important granular computing methods, can effectively solve problems accompanied by uncertain information. We propose one kind of research method for the coordinated development strategy on the basis of a soft fuzzy rough set. The coordinated development strategy model that is subject to the soft fuzzy rough set for the enterprise is constructed in view of concepts for ecological environment protection. The effect for the enterprise’s coordinated development strategy is predicted to get a kind strategy prediction set for each coordinated development strategy. Effectiveness for algorithm is verified in the experimental result. Since the decision-making method using the soft fuzzy rough set combines the advantages of soft fuzzy and rough set, it can deal with fuzzy uncertain and incomplete information well, and it has been widely used in intelligent decision-making. Next research can combine other uncertain mathematical models such as dual-domain rough sets, multigranularity rough sets, and hesitant fuzzy sets, while finding practical applications for scene cases. Algorithm performance shall be further analyzed, and further optimization shall be considered, and then collect the decision rules aimed to further improve the accuracy of enterprise’s coordinated development strategy decision-making.

All the data used to support the findings of this study are available from the corresponding author upon request.

The authors declare that they have no conflicts of interest.