Title
A design method for multilayer feedforward neural networks for simple hardware implementation
Abstract
A method for designing a multiplierless multilayer feedforward neural network for continuous input-output mapping is presented. This method uses the simplified sigmoid activation functions at the weights in the output layer, 3-level discrete quantization functions at the hidden neurons, and single powers-of-two weights in the input layer. When tested with noisy vectors, the multiplierless network can achieve high recall accuracy, while having increased computational speed in practical applications and reduced hardware cost in digital implementation
Year
DOI
Venue
1993
10.1109/ISCAS.1993.394238
Chicago, IL
Keywords
Field
DocType
feedforward neural nets,multilayer perceptrons,quantisation (signal),computational speed,continuous input-output mapping,digital implementation,hardware implementation,hidden neurons,multilayer feedforward neural networks,noisy vectors,output layer,recall accuracy,simplified sigmoid activation functions,single powers-of-two weights,three-level discrete quantization
Feedforward neural network,Computer science,Activation function,Probabilistic neural network,Electronic engineering,Time delay neural network,Computer hardware,Quantization (signal processing),Sigmoid function
Conference
ISBN
Citations 
PageRank 
0-7803-1281-3
0
0.34
References 
Authors
2
2
Name
Order
Citations
PageRank
Hon Keung Kwan129545.33
Chuan Zhang Tang200.34