By Ya. Z. Tsypkin (auth.), Julius T. Tou (eds.)

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Rozonoer, Theoretical Basis of the Method of Potential Functions in the Problem of Teaching Automata to Separate Input Situations into Classes," Avtomat. i Telemekhan. 2S (6) (1964). 7. I. P. Devyaterikov, A. I. Propoi, and Va. Z. Tsypkin, On Recursive Algorithms for Learning Pattern Recognition," Avtomat. i Telemekhan. 27 (I) (1967). 8. M. I. Shlezinger, On Self-Judging Pattern Separation, in: "Reading Automata," Naukova Dumka, Kiev (1965). 9. J. Spragins, Learning without a Teacher, IEEE Trans.

D. , New York (1960). 4. A. A. Feldbaum, "Optimal Control Systems," Academic Press, New York (1966). 5. V. A. Yakubovich, Some General Theoretical Principles of Designing Learning Recognizing Systems, in: "Computing Techniques and Programming Questions," Vol. 4, Leningrad State University, Leningrad (1965). 6. M. A. Aizerman, E. M. Braverman, and L. I. Rozonoer, Theoretical Basis of the Method of Potential Functions in the Problem of Teaching Automata to Separate Input Situations into Classes," Avtomat.

The block schematic of a learning receiver based on the algorithm of Eq. (111) is shown in Fig. 7. , the input signal directly, as shown by the dashed line in Fig. 7. 29). 2. , useful signals and noise, or signals of large and of small amplitude, etc. In this situation no information to supplement the input signals is supplied to the receiver. y Fig. 7 Sec. 5] 29 Learning Inventory Planning We consider here the case when the signals x constitute scalar functions of time, and the average risk of an erroneous classification, given by Eq.

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