A Learning Criterion for Stochastic Rules
Machine Learning - Computational learning theory
Fast discovery of association rules
Advances in knowledge discovery and data mining
Introduction to artificial life
Introduction to artificial life
Knowledge Discovery and Measures of Interest
Knowledge Discovery and Measures of Interest
Get Real! XCS with Continuous-Valued Inputs
Learning Classifier Systems, From Foundations to Applications
A real-coded genetic algorithm using the unimodal normal distribution crossover
Advances in evolutionary computing
Cognitive systems based on adaptive algorithms
ACM SIGART Bulletin
Extracted global structure makes local building block processing effective in XCS
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Kernel-based, ellipsoidal conditions in the real-valued XCS classifier system
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Hyper-ellipsoidal conditions in XCS: rotation, linear approximation, and solution structure
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Flexible learning of problem solving heuristics through adaptive search
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 1
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This paper proposes a new method of developing a process response model from continuous time-series data. Chemical and/or biotechnical plants always generate a large amount of time series data. However, since conventional process models are described as a set of control models, it is difficult to explain complicated and active plant behaviors. To uncover complex plant behaviors, the method consists of the following phases: (1) Reciprocal correlation analysis; (2) Process response model; (3) Extraction of a workflow; (4) Extraction of control rules of real-valued data. The main contribution of the research is to establish a method to mine a set of meaningful control rules from a Learning Classifier System using UNDX(Unimodal Normal Distribution Crossover) for real-valued data. The proposed method has been applied to an actual process of a biochemical plant and has shown its validity and effectiveness.