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A learning algorithm for optimal representation of experimental data. (English) Zbl 0811.90018

Summary: We have developed a procedure for finding optimal representations of experimental data. Criteria for optimality vary according to context: an optimal state space representation will be one that best suits one’s stated goal for reconstrution. We consider an \(\infty\)-dimensional set of possible reconstruction coordinate systems that include time delays, derivatives, and many other possible coordinates; and any optimality criterion is specified as a real valued functional on this space. We present a method for finding the optima using a learning algorithm based upon the genetic algorithm and evolutionary programming. The learning algorithm machinery for finding optimal representations is independent of the definition of optimality, and thus provides a general tool useful in a wide variety of contexts.

MSC:

91B84 Economic time series analysis
68T05 Learning and adaptive systems in artificial intelligence
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