科创中国

高维数据的稀疏性问题是降低协同过滤技术推荐质量的主要原因之一.提出了基于径向基函数网络(RBFN)一项目聚类的算法来降低数据的稀疏性,应用径向基函数网络(RBFN)处理高维稀疏数据得到一个完整的矩阵,应用基于项目聚类的协同过滤推荐算法产生推荐.实验结果表明,本算法比其他算法能更好处理协同过滤中的稀疏性问题.

The rapid expansion of enterprises makes product collaborative design(PCD)a critical issue under the distributed heterogeneous environment,but as the collaborative task of large-scale network becomes more complicated,neither unified task decomposition and allocation methodology nor Agent-based network management platform can satisfy the increasing demands.In this paper,to meet requirements of PCD for distributed product development,a collaborative design mechanism based on the thought of modularity and the Agent technology is presented.First,the top-down 4-tier process model based on task-oriented modular and Agent is constructed for PCD after analyzing the mapping relationships between requirements and functions in the collaborative design.Second,on basis of sub-task decomposition for PCD based on a mixed method,the mathematic model of task-oriented modular based on multi-objective optimization is established to maximize the module cohesion degree and minimize the module coupling degree,while considering the module executable degree as a restriction.The mathematic model is optimized and simulated by the modified PSO,and the decomposed modules are obtained.Finally,the Agent structure model for collaborative design is put forward,and the optimism matching Agents are selected by using similarity algorithm to implement different task-modules by the integrated reasoning and decision-making mechanism with the behavioral model of collaborative design Agents.With the results of experimental studies for automobile collaborative design,the feasibility and efficiency of this methodology of task-oriented modular and Agent-based collaborative design in the distributed heterogeneous environment are verified.On this basis,an integrative automobile collaborative R&D platform is developed.This research provides an effective platform for automobile manufacturing enterprises to achieve PCD,and helps to promote product numeralization collaborative R&D and management development.

The current research of the decomposition methods of complex optimization model is mostly based on the principle of disciplines, problems or components. However, numerous coupling variables will appear among the sub-models decomposed, thereby make the efficiency of decomposed optimization low and the effect poor. Though some collaborative optimization methods are proposed to process the coupling variables, there lacks the original strategy planning to reduce the coupling degree among the decomposed sub-models when we start decomposing a complex optimization model. Therefore, this paper proposes a decomposition method based on the global sensitivity information. In this method, the complex optimization model is decomposed based on the principle of minimizing the sensitivity sum between the design functions and design variables among different sub-models. The design functions and design variables, which are sensitive to each other, will be assigned to the same sub-models as much as possible to reduce the impacts to other sub-models caused by the changing of coupling variables in one sub-model. Two different collaborative optimization models of a gear reducer are built up separately in the multidisciplinary design optimization software iSIGHT, the optimized results turned out that the decomposition method proposed in this paper has less analysis times and increases the computational efficiency by 29.6%. This new decomposition method is also successfully applied in the complex optimization problem of hydraulic excavator working devices, which shows the proposed research can reduce the mutual coupling degree between sub-models. This research proposes a decomposition method based on the global sensitivity information, which makes the linkages least among sub-models after decomposition, and provides reference for decomposing complex optimization models and has practical engineering significance.