基于OPCA-IGAFNN的NQI综合服务 信息平台服务质量评价

针对传统模糊神经网络（FNN）评价模型在国家质量基础设施（NQI）综合服务信息 平台的服务质量评价中存在收敛速度慢、易陷入局部最优解等问题，提出一种基于优化主成 分分析法（OPCA）与改进遗传算法（IGA）的模糊神经网络智能评价方法. 为提高FNN的网络收 敛速度，利用OPCA根据评价指标间的相关性，删除冗余指标因素，减少网络输入量，实现对网 络输入的降维处理；将 IGA与 FNN相结合，利用自适应的交叉与变异概率对 FNN隶属函数的 系数进行全局搜索，克服 FNN在智能评价时容易陷入局部极值问题 . 基于我国实际的 NQI综 合服务信息平台服务质量调研数据开展试验分析，结果表明，OPCA-IGAFNN评价模型具有高 效、准确的评价效果.

Service Quality Evaluation for NQI Comprehensive Service Platform Based on OPCA-IGAFNN

Aiming at the problems of the traditional Fuzzy Neural Network (FNN) evaluation model in the ser? vice quality evaluation of the National Quality Infrastructure (NQI) comprehensive service information platform, such as slow convergence speed and likely falling into the local optimal solution, a fuzzy neural network intelligent evalua? tion method based on Optimized Principal Component Analysis (OPCA) and Improved Genetic Algorithm (IGA) was proposed. In order to improve the network convergence speed of FNN, OPCA was used to delete redundant index fac? tors reduce the amount of network input, and realize the dimensionality reduction of network input, according to the correlation between evaluation indexes. Then, IGA is combined with FNN, and the coefficients of the membership function of FNN are searched globally by using adaptive crossover and mutation probability, so as to overcome the problem that FNN is easy to fall into local extremum in intelligent evaluation effectively. Based on the actual service quality survey data of the NQI platform in China, the experimental analysis shows that the OPCA-IGAFNN evalua? tion model has a more efficient and accurate evaluation effect.