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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.

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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