Abstract
The most common type of noise in continuous systems of the real world is Gaussian noise, whereas discrete environments are usually subject to noise of a discrete type. The established, original solution for on-line inference of finite automata that is based on generalized recurrent neural networks is evaluated in the presence of noise of both types. It showed quite good performance and robustness.
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Gabrijel, I., Dobnikar, A. (2003) On-line identification and reconstruction of finite automata with generalized recurrent neural networks. Neural networks 16(1): 101–120.
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Gabrijel, I., Dobnikar, A. (2005). On-line Inference of Finite Automata in Noisy Environments. In: Ribeiro, B., Albrecht, R.F., Dobnikar, A., Pearson, D.W., Steele, N.C. (eds) Adaptive and Natural Computing Algorithms. Springer, Vienna. https://doi.org/10.1007/3-211-27389-1_32
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DOI: https://doi.org/10.1007/3-211-27389-1_32
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-24934-5
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