References
- Back, A. D. and Tsoi, A. C. (1991). FIR and IIR synapses, A new neural network architecture for time series modelling, Neural Computation 3(3): 375-385.
- Campolucci, P. and Piazza, F. (2000). Intrinsic stability-control method for recursive filters and neural networks, IEEE Transactions on Circuit and Systems—II: Analog and Digital Signal Processing 47(8): 797-802.
- Cannas, B., Cincotti, S., Marchesi, M. and Pilo, F. (2001). Learnig of Chua's circuit attractors by locally recurrent neural networks, Chaos Solitons & Fractals 12(11): 2109-2115.
- Cao, J., Yuan, K. and Li, H. (2006). Global asymptotical stability of recurrent neural networks with multiple discrete delays and distributed delays, IEEE Transactions on Neural Networks 17(6): 1646-1651.
- Ensari, T. and Arik, S. (2005). Global stability analysis of neural networks with multiple time varying delays, IEEE Transactions on Automatic Control 50(11): 1781-1785.
- Fasconi, P., Gori, M. and Soda, G. (1992). Local feedback multilayered networks, Neural Computation 4(1): 120-130.
- Forti, M., Nistri, P. and Papini, D. (2005). Global exponential stability and global convergence in finite time of delayed neural networks with infinite gain, IEEE Transactions on Neural Networks 16(6): 1449-1463.
- Gori, M., Bengio, Y. and Mori, R. D. (1989). BPS: A learning algorithm for capturing the dynamic nature of speech, International Joint Conference on Neural Networks, Washington DC, USA, Vol. II, pp. 417-423.
- Gupta, M. M., Jin, L. and Homma, N. (2003). Static and Dynamic Neural Networks. From Fundamentals to Advanced Theory, John Wiley & Sons, Hoboken, NJ.
- Gupta, M. M. and Rao, D. H. (1993). Dynamic neural units with application to the control of unknown nonlinear systems, Journal of Intelligent and Fuzzy Systems 1(1): 73-92.
- Marcu, T., Mirea, L. and Frank, P. M. (1999). Development of dynamical neural networks with application to observer based fault detection and isolation, International Journal of Applied Mathematics and Computer Science 9(3): 547-570.
- Patan, K. (2007). Stability analysis and the stabilization of a class of discrete-time dynamic neural network, IEEE Transactions on Neural Networks 18(3): 660-673.
- Patan, K. (2008a). Aproximation of state-space trajectories by locally recurrent globally feed-forward neural networks, Neural Networks 21(1): 59-64.
- Patan, K. (2008b). Artificial Neural Networks for the Modelling and Fault Diagnosis of Technical Processes, Lecture Notes in Control and Information Sciences, Vol. 377, Springer-Verlag, Berlin.
- Patan, K. (2008c). Stability criteria for three-layer locally recurrent networks, Proceedings of the 17th IFAC World Congress on Automatic Control, Seoul, Korea, (on CDROM).
- Patan, K. and Parisini, T. (2005). Identification of neural dynamic models for fault detection and isolation: The case of a real sugar evaporation process, Journal of Process Control 15(1): 67-79.
- Patan, K., Witczak, M. and Korbicz, J. (2008). Towards robustness in neural network based fault diagnosis, International Journal of Applied Mathematics and Computer Science 18(4): 443-454, DOI: 10.2478/v10006-008-0039-2.
- Tsoi, A. C. and Back, A. D. (1994). Locally recurrent globally feedforward networks: A critical review of architectures, IEEE Transactions on Neural Networks 5(2): 229-239.
- Xiang-Qun, L. and Zhang, H. Y. (2000). Fault detection and diagnosis of permanent-magnet DC motor based on parameter estimation and neural network, IEEE Transactions on Industrial Electronics 47(5): 1021-1030.
- Zhang, J., Morris, A. J. and Martin, E. B. (1998). Long term prediction models based on mixed order locally recurrent neural networks, Computers Chemical Engineering 22(7-8): 1051-1063.