提高故障空间分界能力的S形-二次函数前向阶层型网络

CIESC Journal

提高故障空间分界能力的S形-二次函数前向阶层型网络

赵晓光,陈丙珍,何小荣

清华大学化工系!北京100084,清华大学化工系!北京100084,清华大学化工系!北京100084

出版日期:1995-08-25 发布日期:1995-08-25

SIGMOIDAL - QUADRATIC BASIS FUNCTION NETWORK FOR IMPROVING THE BOUNDING CAPABILITY OF FAULT SPACES

Zhao Xiaoguang, Chen Bingzhen and He Xiaorong(Department of Chemical Engineering ,Tsinghua University , Beijing 100084)

Online:1995-08-25 Published:1995-08-25

摘要/Abstract

摘要： 提出了一种S形-二次函数前向阶层型网络(Sigmoidal-Quadratic Basis Func- tion Network,简称SQBFN),首先以二次超曲面对输入空间进行初始划分,并经过叠合形成复杂的决策空间,以达到对故障类区域的精确描述。理论分析和实例研究结果表明,SQBFN具有分类能力强、柔性高,并可按需要形成封闭或半封闭的定界空间,可避免对空白空间的任意划分。

Abstract: According to the characteristics and requirements of fault diagnosis in process systems and based on the previous work, this paper puts forward a Sigmoidal - Quadratic Basis Function Network (SQBFN) which can improve the bounding capability of fault space. This netv/ork first divides the input space by quadratic hypersurfaces and then exactly describes the regions of faults in fault space through complicated decision space formed by the superposition of those hypersurfaces. SQBFN is of powerful classification, good flexibility and may form the required closed or semi - closed decision spaces avoiding arbitrarily dividing blank spaces. This paper gives an introduction to SQBFN and verifies its effectiveness by two examples. The results of theoretical analysis and researches on examples indicate that the bounding capability of SQBFN is superior to other networks and at the same time its structure is not complicated and even more simple.

中图分类号:

O241

赵晓光,陈丙珍,何小荣. 提高故障空间分界能力的S形-二次函数前向阶层型网络 [J]. CIESC Journal.

Zhao Xiaoguang, Chen Bingzhen and He Xiaorong(Department of Chemical Engineering ,Tsinghua University , Beijing 100084). SIGMOIDAL - QUADRATIC BASIS FUNCTION NETWORK FOR IMPROVING THE BOUNDING CAPABILITY OF FAULT SPACES[J]. .