Group decision making of domain experts is the most direct approach to determine loss function in three-way decision problems. Different from linguistic variable model and fuzzy set model with single uncertainty, expert evaluations described by cloud model can reflect the complex uncertainty form in cognitive process, and the synthetic evaluation function can be obtained by cloud aggregation. However, numerical characteristics only are performed simple linear combination in current cloud aggregation methods, leading the lack of the description of concept semantic differences and the difficulty to obtain convincing results. Therefore firstly, the weighted distance sum was proved to be a convex function in the distance space of cloud model. And the aggregational cloud model was defined as the minimum point of that function. Then, this definition was generalized to the multi-cloud model scenario, and a cloud aggregation method namely density center based cloud aggregation method was proposed. In group decision making, the proposed method obtains the most accurate synthetic evaluations with the highest similarity between synthetic evaluation and basic evaluation, providing a novel semantic interpretation of the determination of loss function. The experimental results show that the misclassification rate of the three-way decision with loss function determined by the proposed method is the lowest compared with simple linear combination and rational granularity methods.

Extreme Learning Machine (ELM) has become a new method in soft-sensing due to its good generalization and fast training speed. However, ELM often needs more hidden layer nodes and its generalization is reduced in the parameter modeling for aluminum electrolysis production process. To solve the problem, a soft-sensing model based on Improved Extreme Learning Machine (IELM) was proposed. Firstly, rough set theory was applied to reduce the unnecessary, unrelated or reductant input variables, reducing the complexity of ELM input. After analyzing the relationship between the input variables and output variables by partial correlation coefficient, the input data was divided into two parts, namely the positive part and the negative part. Then, the corresponding ELM model was built according to the two parts. Finally, the soft-sensing model of molecular ratio based on the improved ELM was built. The simulation experimental results show that the soft-sensing model based on the IELM has better generalization and stability.

The Density Peak Clustering (DPC) algorithm which required few parameters and no iteration was proposed in 2014, it was simple and novel. In this paper, a grid clustering algorithm which could efficiently deal with large-scale data was proposed based on DPC. Firstly, the N dimensional space was divided into disjoint rectangular units, and the unit space information was counted. Then the central cells of space was found based on DPC, namely, the central cells were surrounded by other grid cells of low local density, and the distance with grid cells of high local density was relatively large. Finally, the grid cells adjacent to their central cells were merged to obtain the clustering results. The experimental results on UCI artificial data set show that the proposed algorithm can quickly find the clustering centers, and effectively deal with the clustering problem of large-scale data, which has a higher efficiency compared with the original density peak clustering algorithm on different data sets, reducing the loss of time 10 to 100 times, and maintaining the loss of accuracy at 5% to 8%.

Facial feature change occur in varying degrees with the increase of age. These features involve the shape features and texture features, which increase the difficulty of face recognition. In order to accurately describe the facial features with change in age to improve the accuracy of face recognition, firstly, this paper applied the innovation of Gabor wavelet on the pyramid model to structure the Gabor pyramid characteristic sequence. This paper used mean grid to descend and denoise the Gabor pyramid characteristic sequence initially, and then reconstructed pyramid characteristic sequence of different samples at the same level and direction. Finally, this paper constructed forty parallel classifiers to classify by using Direct Fractional-step Linear Discriminant Analysis (DF-LDA) algorithm. The experimental results show that the Gabor pyramid characteristic sequence can improve the accuracy of face recognition with the change in age.