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 :
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