@inbook{88966855477a4b039ccc5d5a5871112b,
title = "Efficient Error Based Metrics for Fuzzy-Neural Network Performance Evaluation",
abstract = "In this paper the effectiveness of different error metrics for assessment of the capabilities of an advanced fuzzy-neural architecture are studied. The proposed structure combines the potentials of the Intuitionistic Fuzzy Logic with the simplicity of the Neo-Fuzzy Neuron theory for implementation of robust modeling mechanisms, able to capture uncertain variations in the data space. A major concern when evaluating the performance of such kind of models is the selection of appropriate error metrics in order to assess their potential to capture a wide range of system behaviours. Therefore, different error metrics to evaluate the functional properties of a proposed Intuitionistic Neo-fuzzy network are studied and a comparative analysis in modeling of chaotic time series is made.",
author = "Margarita Terziyska and Yancho Todorov and Maria Dobreva",
year = "2018",
month = jan,
day = "1",
doi = "10.1007/978-3-319-65530-7_17",
language = "English",
isbn = "978-331965529-1",
series = "Studies in Computational Intelligence",
publisher = "Springer",
pages = "185--201",
editor = "Michail Todorov and Ivan Georgiev and Krassimir Georgiev and Ivan Georgiev",
booktitle = "Advanced Computing in Industrial Mathematics - 11th Annual Meeting of the Bulgarian Section of SIAM, Revised Selected Papers",
address = "Germany",
note = "11th Annual Meeting of the Bulgarian Section of the Society for Industrial and Applied Mathematics, BGSIAM 2016 ; Conference date: 20-12-2016 Through 22-12-2016",
}