Abstract
The feasibility of identifying an unknown nonlinear stochastic system as belonging to a class of such systems by use of pattern recognition methods has recently been demonstrated. This paper examines the performance aspects of the classification technique by means of a theoretical approximation to the Bayes minimum classification error. The error is shown to be strongly dependent upon the pattern vector dimension thus showing that the classification error can be controlled by appropriate choice of pattern dimension. Two detailed classification examples are included, the first of which may be compared to earlier experimental results to substantiate the approximations used.
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References
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© 1974 Plenum Press, New York
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Hofstadter, R.F., Saridis, G.N. (1974). Performance Aspects of Stochastic Nonlinear System Classification by Pattern Recognition Methods. In: Fu, K.S., Tou, J.T. (eds) Learning Systems and Intelligent Robots. Springer, Boston, MA. https://doi.org/10.1007/978-1-4684-2106-4_5
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DOI: https://doi.org/10.1007/978-1-4684-2106-4_5
Publisher Name: Springer, Boston, MA
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