基于自组织膜计算的案例推理属性权重优化方法

为了解决案例推理检索过程中属性权重的合理分配问题,提出了自组织膜计算属性权重优化方法.首先建立了衡量权重分布是否合理的适应度函数,并设计了细胞型的单层膜结构以及对权重对象集进行膜计算寻优的选择、交叉、变异和双向交流规则,然后根据适应度函数和设计的规则训练并确定基本膜的个数,从而得到合理分配属性权重的自组织膜计算算法.最后通过UCI中的5个回归数据集和某污水处理过程溶解氧质量浓度的生产数据进行回归实验,结果表明:该方法应用于案例推理过程可以有效降低回归的拟合误差,说明自组织膜计算方法分配的属性权重比较合理,可以进一步提高案例推理模型的求解性能.

Self-organization Membrane Computing-based Attribute Weights Optimization for Case-based Reasoning Model

To solve the problem of the distribution of the attribute weights in the retrieval process based on case-based reasoning ( CBR ) , a self-organizing membrane computing method was proposed to calculate the attribute weights. Firstly, the fitness function was established to evaluate the rationality of the weight distribution, and the one level membrane structure with cell type and the membrane rule with selection, crossover, mutation and two-way communication were designed to search the optimal weight object set iteratively. Then, according to the fitness function and rules designed for training. The number of basic membrane and the reasonable value of attribute weights was obtained. Finally, 5 regression data sets from UCI and the dissolved oxygen concentration data from the wastewater treatment process were used to carry out a comparison experiment. The results show that the proposed method can effectively reduce the fitting error of regression and receive the reasonable distribution of attribute weights, thus to further improve the solution to the performance of CBR model.