Characterization of analog local cluster neural network hardware for control.
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.
School of Software Engineering and Data Communication, Queensland University of Technology, Brisbane, Qld. 4001, Australia. j.sitte@qut.edu.au
Joaquin
J
Sitte
Liang
L
Zhang
Ulrich
U
Rueckert
D000465:Algorithms; D016000:Cluster Analysis; D003198:Computer Simulation; D003661:Decision Support Techniques; D004867:Equipment Design; D019544:Equipment Failure Analysis; D005246:Feedback; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*