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I. Aizenberg. Computational Intelligence, Theory and Application" (B. Reusch Editor), Springer, Berlin, Heidelberg, New York, 2006, pp. 457-471
 

Summary: I. Aizenberg. Computational Intelligence, Theory and Application" (B. Reusch Editor),
Springer, Berlin, Heidelberg, New York, 2006, pp. 457-471
Solving the Parity n Problem and Other Nonlinearly
Separable Problems Using a Single Universal Binary
Neuron
Igor Aizenberg
Texas A&M University-Texarkana,
P.O. Box 5518, 2600 N. Robison Rd. Texarkana, Texas 75505 USA;
igor.aizenberg@tamut.edu
Abstract. A universal binary neuron (UBN) operates with the complex-valued
weights and the complex-valued activation function, which is the function of
the argument of the weighted sum. This makes possible the implementation of
the nonlinearly separable (non-threshold) Boolean functions on the single
neuron. Hence the functionality of the UBN is incompatibly higher than the
functionality of the traditional perceptron, because this neuron can implement
the non-threshold Boolean functions. The UBN is closely connected with the
discrete-valued multi-valued neuron (MVN). This is also a neuron with the
complex-valued weights and the complex-valued activation function, which is
the function of the argument of the weighted sum. A close relation of the MVN
and UBN and of the multiple-valued threshold functions and P-realizable

  

Source: Aizenberg, Igor - College of Science, Technology, Engineering, and Mathematics, Texas A&M University at Texarkana

 

Collections: Computer Technologies and Information Sciences