A NOVEL NEURAL NETWORK-BASED APPROACH FOR DESIGNING COMPLEX FIR FILTERS

Zhao Hui; Yu Juebang

Volume 20 Issue 3

May 1998

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Article Navigation > Journal of Electronics & Information Technology > 1998 > 20(3): 397-403

Zhao Hui, Yu Juebang. A NOVEL NEURAL NETWORK-BASED APPROACH FOR DESIGNING COMPLEX FIR FILTERS[J]. Journal of Electronics & Information Technology, 1998, 20(3): 397-403.

Citation:

Zhao Hui, Yu Juebang. A NOVEL NEURAL NETWORK-BASED APPROACH FOR DESIGNING COMPLEX FIR FILTERS[J]. Journal of Electronics & Information Technology, 1998, 20(3): 397-403.

Zhao Hui, Yu Juebang. A NOVEL NEURAL NETWORK-BASED APPROACH FOR DESIGNING COMPLEX FIR FILTERS[J]. Journal of Electronics & Information Technology, 1998, 20(3): 397-403.

Citation:

Zhao Hui, Yu Juebang. A NOVEL NEURAL NETWORK-BASED APPROACH FOR DESIGNING COMPLEX FIR FILTERS[J]. Journal of Electronics & Information Technology, 1998, 20(3): 397-403.

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A NOVEL NEURAL NETWORK-BASED APPROACH FOR DESIGNING COMPLEX FIR FILTERS

Zhao Hui^{①;虞厥邦},
Yu Juebang

Received Date: 1996-11-22
Rev Recd Date: 1997-07-18
Publish Date: 1998-05-19

Abstract

Abstract

A novel complex FIR filter design approach based on Neural Network Optimiza-tion(NNO) technique is proposed in this paper. To demonstrate the feasibility of the NNO design approach, the weight least mean square criterion between the disired frequency response and the designed filter response is defined as the Lyapunov energy function of a continuous Hopfield network, and the network state equations are drived. The implementation of the NNO approach is described together with some design guidelines. A few design examples are given and the advantages of NNO approach over conventional methods are illustrated.

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References(1)

References

Xu D J, Daley M L. Design of optimal digital filter using a parallel genetic algorithm. IEEE Trans. on CAS- n，1995,42(10): 673-675.[2]Karam L J. Complex Chebyshev approximation for FIR filter design. IEEE Trans. on CAS-II, 1995, 42(3): 207-215.[3]Komodromes M Z, et al. Design of FIR filter with complex desired frequency response using a generalized Remez algorithm. IEEE Trans. on CAS- II，1995, 42(2): 274-278.[4]焦李成.神经网络计算.西安:西安电子科技大学出版社,1993,第二章.[5]Hopfield J J, Tank D W. Neural computation of decision on optimization problems. Bio. Cyb. 1985, 52(3): 141-152.[6]Lightstone M, et al. Efficient frequency-sampling design of one- and two-dimensional FIR filters using structural subband decomposition. IEEE Trans. on CAS- II，1994,41(3): 189-201.[7]Zhao H.[J].Yu J-B. A novel neural network-based approach for design digital filters, in Proc. ISCAS 97/IEEE, Hong Kong.2272,1997:-

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