文献类型：期刊文献

英文题名：BP neural network modeling with sensitivity analysis on monotonicity based Spearman coefficient

作者：Zhou, Yang[1];Li, Shaojun[1]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China

年份：2020

卷号：200

外文期刊名：CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

收录：;EI(收录号：20242616362460);WOS:【SCI-EXPANDED(收录号:WOS:000527299600009)】；

基金：The authors of this paper appreciate the National Natural Science Foundation of China (under Project No. 21676086) and the Fundamental Research Funds for the Central Universities (222201917006) for their financial support.

语种：英文

外文关键词：Spearman coefficient; Monotonicity; Soft sensing; BP neural network

摘要：This paper proposes a new monotonicity extraction method which is used to constrain the modeling process of a neural network. The main contributions of this paper are the sensitivity analysis on monotonicity based on Spearman coefficient, and the application of monotonicity on neural network modeling. This study uses scatter plots of bivariate variables and the Spearman coefficient to extract the monotonic information. To weaken the influence of noise, binary 0-1 integer linear program is applied to filter the scatter diagram. Based on the monotonicity information, a constraint optimization problem is proposed to obtain the BP neural network model and an Alopex-based evolutionary algorithm (AEA) is used to search for the optimal weights and thresholds. The results of a numeral example and an ethylene cracking furnace show that the proposed approach can achieve a good predicting performance in the two cases.