Abstract
This paper presents a Nonlinear Neuro-Fuzzy Network (NNFN) for equalization of channel distortion. The NFNN is constructed by using fuzzy rules that incorporate nonlinear functions. The structure and learning algorithm of NNFN are presented and the development of adaptive equalizer has been performed. The equalizer is applied for equalization of channel distortion of time-invariant and time-varying channels. The performance of NNFN based equalizer has been compared with the performance of other nonlinear equalizers. The obtained simulation results of NNFN based system satisfies the effectiveness of the proposed system in equalization of channel distortion.
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Abiyev, R., Mamedov, F., Al-shanableh, T. (2007). Nonlinear Neuro-fuzzy Network for Channel Equalization. In: Melin, P., Castillo, O., Ramírez, E.G., Kacprzyk, J., Pedrycz, W. (eds) Analysis and Design of Intelligent Systems using Soft Computing Techniques. Advances in Soft Computing, vol 41. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72432-2_33
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DOI: https://doi.org/10.1007/978-3-540-72432-2_33
Publisher Name: Springer, Berlin, Heidelberg
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