@article {3012,
	title = {The Temporal Neuro-Fuzzy Systems Learning Using Artificial Immune Algorithm, ISSN/ISBN 1970 \&ndash; 8734/1970-8742},
	journal = {IREME International Review of Mechanical Engineering},
	volume = {Vol 4},
	number = {N{\textdegree} 1},
	year = {2012},
	pages = {pp: 918-922},
	abstract = {In this work we propose an immune approach for learning neurofuzzy systems, namely NEFDIAG (NEuro Fuzzy DIAGnosis). NEFDIAG is a software devoted primarily to creation, training and test of a classification Neuro-Fuzzy system of industrial process failures. But in case of great number of input variables NEFDIAG structure grows essentially and the dimensionality of learning task becomes a problem. Existing methods of NEFDIAG learning allow only identifying parameters of NEFDIAG without modifying its structure. We propose an immune Artificial learning approach for NEFDIAG learning based on clonal selection and immune network theories. It allows not only to identify NEFDIAG parameters but also to reduce number of neurons in hidden layers (rules layer) of NEFDIAG.},
	url = {https://www.researchgate.net/publication/290061642_The_temporal_Neuro-Fuzzy_systems_learning_using_artificial_immune_algorithm},
	author = {Mahdaoui Rafik and Mouss Leila Hayet and Mouss Med Djamel}
}
