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Systolic neural network architecture for second order hidden Markov models

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4 Author(s)
Feng Zhaozhi ; National Lab. of Pattern Recognition, Inst. of Autom., Acad. Sinica, Beijing, China ; Huang Zailu ; Chen Daowen ; Wan Faguan

This paper presents a systolic neural network architecture for implementing second order hidden Markov models (SOHMMs). A programmable systolic arrays is proposed. A unified model for recurrent high order multilayer feedforward neural networks and SOHMMs is exploited for the architecture design. Extended Viterbi algorithm for SOHMMs is described. Finally, the implementation based on TMS320C25 chip is also discussed

Published in:

Pattern Recognition, 1992. Vol. IV. Conference D: Architectures for Vision and Pattern Recognition, Proceedings., 11th IAPR International Conference on

Date of Conference:

30 Aug-3 Sep 1992