基于BPSA算法价格满意度的评价与优化

Evaluation and optimization of price satisfaction based on BPSA algorithm

为了对烟草行业价格满意度进行优化和评价,提出了客户价格满意度的基本思想,在简要阐述模拟退火神经网络(BPSA)算法的基础上建立客户价格满意度的非线性模型,并通过BPSA算法实现之。然后对某烟草公司价格满意度的相关数据进行了提取、优化、对比和分析,结果证明,BPSA算法局部加快了学习的收敛速度,克服了BP算法的局限性,能够在客户价格满意度中得到最小极值点区间,是一种行之有效的处理方法,在客户价格满意度方面具有一定的应用价值。

In order to optimize and evaluate price-satisfaction in the tobacco industry,a basic idea is proposed,in which a nonlinear model is built in explaining in brief simulated annealing neural network（BPSA）algorithm and is realized through the BPSA algorithm.And then the price satisfaction data of some tobacco companies are extracted,optimized,compared and analyzed,which results show that BPSA algorithm partly accelerates the convergence speed of learning and overcomes the limitations of BP algorithm,meantime,the minimum extreme point range is obtained in customer satisfaction.Therefore BPSA algorithm is an effective approach and is valuable in application.