This Article 
 Bibliographic References 
 Add to: 
Learning Control Strategies for Chemical Processes: A Distributed Approach
June 1992 (vol. 7 no. 3)
pp. 35-43

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.

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
Usage of this product signifies your acceptance of the Terms of Use.