Characterization of analog local cluster neural network hardware for control
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Updated: Jun 25, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
Published on: March 2, 2015
Joaquin Sitte1, Liang Zhang, Ulrich Rueckert
1School of Software Engineering and Data Communication, Queensland University of Technology, Brisbane, Qld. 4001, Australia. j.sitte@qut.edu.au
Abstract:
The local cluster neural network (LCNN) was designed for analog realization especially suited to applications in control systems. It uses clusters of sigmoidal neurons to generate basis functions that are localized in multidimensional input space. Sigmoidal neurons are well suited to analog electronic realization. In this paper, we report the results of extensive measurements that characterize the computational capabilities of the first analog very large scale integration (VLSI) realization of the LCNN. Despite manufacturing fluctuations and the inherent low precision of analog electronics, the test results suggest that it may be suitable for use in feedback control systems.
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