DocumentCode
1406485
Title
Modeling stripline discontinuities by neural network with knowledge-based neurons
Author
Wang, Bing-Zhong ; Zhao, Deshuang ; Hong, Jingsong
Author_Institution
Inst. of Appl. Phys., Univ. of Electron. Sci. & Technol. of China, Chengdu, China
Volume
23
Issue
4
fYear
2000
fDate
11/1/2000 12:00:00 AM
Firstpage
692
Lastpage
698
Abstract
A three-layer neural network with knowledge-based neurons in the hidden layer (NNKBN) is presented for modeling stripline discontinuities. In NNKBN, prior knowledge for stripline discontinuity is incorporated into each hidden neuron. With knowledge-based neurons, the learning ability and generalization of the neural network are improved. Compared with conventional multi-layer perceptron neural network, the NNKBN can map the input-output relationships with fewer hidden neurons and has higher reliability for extrapolation beyond training data range. Two examples are given to illustrate the potential power of this approach.
Keywords
circuit CAD; digital integrated circuits; extrapolation; high-speed integrated circuits; integrated circuit design; integrated circuit modelling; learning (artificial intelligence); multilayer perceptrons; strip line discontinuities; HSDICs; extrapolation; hidden layer; high-speed digital ICs; input-output relationships; knowledge-based neurons; learning ability; stripline discontinuities; three-layer neural network; training data range; Artificial neural networks; Extrapolation; Multi-layer neural network; Multilayer perceptrons; Neural networks; Neurons; Stripline; Training data; Transmission line discontinuities; Vectors;
fLanguage
English
Journal_Title
Advanced Packaging, IEEE Transactions on
Publisher
ieee
ISSN
1521-3323
Type
jour
DOI
10.1109/6040.883760
Filename
883760
Link To Document