28 February 2005 A CMOS realizable recurrent neural network for signal identification
Author Affiliations +
The architecture of an analog recurrent neural network that can learn a continuous-time trajectory is presented. The proposed learning circuit does not distinguish parameters based on a presumed model of the signal or system for identification. The synaptic weights are modeled as variable gain cells that can be implemented with a few MOS transistors. The network output consists primarily of neuron signals which portray the periodic characteristics of the input signal in unsupervised mode. For the specific purpose of demonstrating the trajectory learning capabilities, a periodic signal with varying characteristics is used. The developed architecture, however, allows for more general learning tasks typical in applications of identification and control. The periodicity of the input signal ensures consistency in the outcome of the error and convergence speed at different instances in time. While alternative on-line versions of the synaptic update measures can be formulated, which allow for faster learning speed and better convergence behavior, the architecture of the analog RNN used here is easier to implement while still allowing to demonstrate the general principle. Because the architecture depends on the network generating a stable limit cycle, and consequently a periodic solution which is robust over an interval of parameter uncertainties, we currently place the restriction of a periodic format for the input signals. The simulated network contains interconnected recurrent neurons with continuous-time dynamics. The system basically performs random-direction descent of the error as a multidimensional extension to the stochastic approximation. To achieve unsupervised learning in recurrent dynamical systems we propose a synapse circuit which has a very simple structure and is suitable for implementation in VLSI.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ganesh Kothapalli, Ganesh Kothapalli, } "A CMOS realizable recurrent neural network for signal identification", Proc. SPIE 5649, Smart Structures, Devices, and Systems II, (28 February 2005); doi: 10.1117/12.582648; https://doi.org/10.1117/12.582648


Beta-CMOS implementation of an artificial neuron
Proceedings of SPIE (March 22 1999)
Non-isotonous beta-driven artificial neuron
Proceedings of SPIE (March 30 2000)
Analog CMOS contrastive Hebbian networks
Proceedings of SPIE (September 16 1992)
Algorithm Development For Neural Networks
Proceedings of SPIE (April 20 1988)
Design of an analog VLSI chip for a neural network...
Proceedings of SPIE (June 01 1991)

Back to Top