Title :
The comparative synapse: a multiplication free approach to neuro-fuzzy classifiers
Author :
Dogaru, Radu ; Chua, Leon O.
Author_Institution :
Dept. of Appl. Electron. & Comput. Sci., California Univ., Berkeley, CA, USA
fDate :
11/1/1999 12:00:00 AM
Abstract :
This paper introduces a novel synaptic model called a comparative synapse. Compared with traditional synapses, the new model is multiplication free, being thus attractive for digital implementations. Our results suggests that in an adaptive layer with binary outputs, the synaptic model does not significantly affect the system performances, provided that the input data is properly projected via a nonlinear preprocessor into a separable space. A set of benchmark classification problems were considered to illustrate this property for the case of the comparative synapse and a nonlinear preprocessor defined by fuzzy membership functions
Keywords :
adaptive signal processing; fuzzy logic; fuzzy neural nets; pattern classification; piecewise linear techniques; signal classification; adaptive layer; benchmark classification problems; binary outputs; comparative synapse; digital implementations; fuzzy membership functions; multiplication free approach; neuro-fuzzy classifiers; nonlinear preprocessor; separable space; Adaptive signal processing; Artificial neural networks; Data preprocessing; Fuzzy logic; Fuzzy neural networks; Fuzzy sets; Neural network hardware; Pattern classification; Performance evaluation; Piecewise linear approximation;
Journal_Title :
Circuits and Systems I: Fundamental Theory and Applications, IEEE Transactions on
