Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Learning methods for radial basis function networks
Future Generation Computer Systems
A Reconfigurable Gaussian/Triangular Basis Functions Computation Circuit
Analog Integrated Circuits and Signal Processing
Fast learning in networks of locally-tuned processing units
Neural Computation
Human expression recognition from motion using a radial basis function network architecture
IEEE Transactions on Neural Networks
Higher-Order-Statistics-Based Radial Basis Function Networks for Signal Enhancement
IEEE Transactions on Neural Networks
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We propose new circuits for the implementation of Radial Basis Functions such as Gaussian and Gaussian-like functions. These RBFs are obtained by the subtraction of two differential pair output currents in a folded cascode configuration. We also propose a multidimensional version based on the unidimensional circuits. SPICE simulation results indicate good functionality. These circuits are intended to be applied in the implementation of radial basis function networks. One possible application of these networks is transducer signal conditioning in aircraft and spacecraft vehicles onboard telemetry systems.