Scalable closed-boundary analog neural networks

IEEE Transactions on Neural Networks
Sied Mehdi FakhraieKenneth Carless Smith

Abstract

In many pattern-classification and recognition problems, separation of different swarms of class representatives is necessary. As well, in function-approximation problems, neurons with a local area of influence have demonstrated measurable success. In our previous work, we have shown how intrinsic quadratic characteristics of traditional metal-oxide-semiconductor (MOS) devices can be used to implement hyperspherical discriminating surfaces in hardware-implemented neurons. In this work, we further extend the concept from quadratic forms to more-arbitrary closed-boundary shapes. Accordingly, we demonstrate how intrinsic characteristics of submicron MOS devices can be utilized to implement efficient pattern discriminators for various applications and, through representative simulations, show their success in some typical function-approximation problems. Further, we offer two mathematical interpretations of possible roles for these networks: Geometrically, we show that our networks employ closed hypercone shapes as their discriminating surfaces; analytically, we show that a set of these synapses connected to a common integrating body calculates the distance between their inputs and weight vectors using a power norm. The feasibility...Continue Reading

References

Jan 1, 1992·IEEE Transactions on Neural Networks·J B Lont, W Guggenbuhl

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Citations

Feb 7, 2007·IEEE Transactions on Neural Networks·Koldo BasterretxeaGuillermo Bosque
Dec 14, 2005·IEEE Transactions on Neural Networks·Yutaka Maeda, Masatoshi Wakamura

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