NDLI: A Pulsed Neural Network With On-Chip Learning and Its Practical Applications

Content Provider	IEEE Xplore Digital Library
Author	Hualiang Zhuang Kay-Soon Low Wei-Yun Yau
Copyright Year	1982
Abstract	This paper proposes a new model for the pulsed neural network. In this model, the information is coded in terms of firing times of pulses that are generated by the neuron. The pulses transmit through the network and excite the dynamics of the neuron. Their synchronism is utilized to design the architecture of the neural network such that it acts as a radial basis function (RBF) network. A new network-learning algorithm is also developed for this pulsed RBF network. The RBF neurons are generated based on the feature of the training data, and the synaptic delays can be adjusted to distribute these RBF neurons in the training data space. The pulse neural network has been implemented compactly with multiplierless approach for both the forward computation and learning algorithm with a field programmable gate array board. As an application demonstration, it is extended to a nonlinear look-up table and applied to estimate the friction occurs in a precision linear stage
Sponsorship	IEEE Industrial Electronics Society
Starting Page	34
Ending Page	42
Page Count	9
File Size	594272
File Format	PDF
ISSN	02780046
Volume Number	54
Issue Number	1
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-02-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Network-on-a-chip Neurons Training data Pulse generation Radial basis function networks Delay Computer networks Field programmable gate arrays Table lookup radial basis function (RBF) neural network Field programmable gate array (FPGA) linear motors pulsed neural network
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Electrical and Electronic Engineering Computer Science Applications

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