One Approach for Training of Recurrent Neural Network Model of IIR Digital Filter
One approach for training of recurrent neural network model of 1-D IIR digital filter is proposed. The sensitivity coefficients method has been applied in the training process of the neural network. The set of time domain data is generated and used as a target function in the training procedure. The modeling results have been obtained for two different cases - for 4-th order bandpass IIR digital filter and for partial response IIR digital filter. The frequency domain behavior of the neural network model and the target IIR filter has been investigated. The analysis of the frequency responses shows good approximation results.
KeywordsNeural Network Model Digital Filter Recurrent Neural Network Magnitude Response Recurrent Neural Network Model
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- Jin L., P. Nikiforuk, and M. Gupta, “Approximation of discrete-time state-space trajectories using dynamical recurrent neural networks”, IEEE Transactions on Automatic Control, Vol. 40, No 7, 1995.Google Scholar
- H. Cruse , Neural Networks as Cybernetic Systems, Brains, Minds and Media, Bielefeld, Germany, October 2006.Google Scholar
- M. Pedersen, “Optimization of recurrent neural networks for time domain series modeling”, Ph.D Thesis, Department of Mathematical Modeling, Technical University of Denmark, 1997.Google Scholar
- Zhe-Zhao Zeng; Ye Chen; Yao-Nan Wang, ”Optimal Design Study of High-Order FIR Digital Filters Based on Neural-Network Algorithm”, International Conference on Machine Learning and Cybernetics, 13-16 Aug. 2006, pp 3157 – 3161.Google Scholar
- Zeng Zhe-zhao; Wen Hui, “Optimal design study of three-type FIR high-order digital filters based on sine basis functions neural-network algorithm”, IEEE International Symposium on Communications and Information Technology, Vol. 2, No 12-14 ,pp. 921- 924, October 2005.Google Scholar
- Wang D. and A. Zilouchian, Intelligent control systems using soft computing methodologies, Editors: Ali Zilouchian, Mo Jamshidi, Prentice Hall, March 2001, pp, 93-110.Google Scholar
- Bhattacharya, D.; Antoniou, A., “Design of IIR filters with arbitrary amplitude and phase responses by feedback neural networks”, IEEE International Symposium on Circuits and Systems, ISCAS ’96, Vol. 3, 12-15 May 1996, pp. 457 – 460.Google Scholar
- W. T. Miller, R. S. Sutton and P. J. Werbos, Neural Networks for Control., Cambridge, MA:MIT Press, 1990.Google Scholar
- Stefanova S., “One Dimensional IIR digital filter modeling based on recurrent neural network”, International Joint Conferences on Computer, Information, and Systems Sciences, and Engineering (CISSE 08), University of Bridgeport, Bridgeport, U.S.A, December 5 - 13, 2008.Google Scholar