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Learning Control Strategies for Chemical Processes: A Distributed Approach
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/64.143237June 1992 (vol. 7 no. 3) pp. 35-43
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Riyaz Sikora, "Learning Control Strategies for Chemical Processes: A Distributed Approach," IEEE Intelligent Systems, vol. 7, no. 3, pp. 35-43, June, 1992.
 
 
BibTex
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@article{ 10.1109/64.143237,
author = {Riyaz Sikora},
title = {Learning Control Strategies for Chemical Processes: A Distributed Approach},
journal ={IEEE Intelligent Systems},
volume = {7},
number = {3},
issn = {0885-9000},
year = {1992},
pages = {35-43},
doi = {http://doi.ieeecomputersociety.org/10.1109/64.143237},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - MGZN
JO - IEEE Intelligent Systems
TI - Learning Control Strategies for Chemical Processes: A Distributed Approach
IS - 3
SN - 0885-9000
SP35
EP43
EPD - 35-43
A1 - Riyaz Sikora,
PY - 1992
VL - 7
JA - IEEE Intelligent Systems
ER -
 

The design of a distributed learning system (DLS) which combines the features of instance-space and hypothesis-space methods is described. This algorithm decomposes a data set of training examples into subsets. After applying an inductive learning program on each subset, it synthesizes the results using a genetic algorithm. It is shown that this parallel distributed approach is more efficient, since each inductive learning program works on only a subset of data. Since the genetic algorithm searches globally in the hypothesis space, this approach gives a more accurate concept description. The implementation of DLS in Common LISP is discussed, and its distributed approach is compared to C4.5 and PLS1 algorithms.

Citation:
Riyaz Sikora, "Learning Control Strategies for Chemical Processes: A Distributed Approach," IEEE Intelligent Systems, vol. 7, no. 3, pp. 35-43, June 1992, doi:10.1109/64.143237
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