Title :
Preprocess identification: an aid for neural net modeling
Author :
Vanlandingham, H.F. ; Choi, J.Y.
Author_Institution :
Bradley Dept. of Electr. Eng., Virginia Polytech. Inst. & State Univ., Blacksburg, VA, USA
Abstract :
This paper presents a method of multivariable system identification which utilized the possible structures of the system to achieve a model which optimally generalizes over the available input/output data. Pseudo-observable forms are defined to illustrate the various structural forms which are possible for the system. The method is useful as a first stage in obtaining a neural net system model. To illustrate the method, data from a portion of a larger chemical process simulator which represents a distillation column having 2-input and 3-outputs is identified
Keywords :
identification; modelling; multivariable systems; neural nets; chemical process simulator; distillation column; input/output data; multivariable system identification; neural net modeling; optimal generalization; preprocess identification; pseudo-observable forms; Chemical processes; Controllability; Counting circuits; Distillation equipment; MIMO; Neural networks; Observability; State-space methods; Transmission line matrix methods; Visualization;
Conference_Titel :
Systems, Man, and Cybernetics, 1994. Humans, Information and Technology., 1994 IEEE International Conference on
Conference_Location :
San Antonio, TX
Print_ISBN :
0-7803-2129-4
DOI :
10.1109/ICSMC.1994.399908
