@conference { ISI:000261578000113,
	title = {Functional Equivalence between Radial Basis Function Neural Networks and Fuzzy Analogy in Software Cost Estimation},
	booktitle = {2008 3RD INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGIES: FROM THEORY TO APPLICATIONS, VOLS 1-5},
	year = {2008},
	note = {3rd International Conference on Information and Communication Technologies, Damascus, SYRIA, APR 07-11, 2008},
	pages = {615+},
	abstract = {We show in this paper the equivalence between the radial basis function networks and Fuzzy analogy in the field of software cost estimation. We prove that under weak conditions, the three layers of RBFN are functionally equivalent to the three steps of fuzzy analogy. This functional equivalence implies that advances in each literature, such new learning rules or new similarity measures, can be applied to both models directly. Furthermore, this equivalence can help us to provide a natural interpretation of cost estimation models based on RBFN.},
	isbn = {978-1-4244-1751-3},
	author = {Idri, Ali and Zakrani, Abdelali and Abran, Alain}
}
@conference { ISI:000259298600002,
	title = {Software cost estimation models using Radial Basis Function Neural Networks},
	booktitle = {SOFTWARE PROCESS AND PRODUCT MEASUREMENT},
	series = {Lecture Notes in Computer Science},
	volume = {4895},
	year = {2008},
	note = {Joint Meeting of the International Workshop on Software Measurement (IWSM)/International Conference on Software Process and Product Measurement (MENSURA), Palma de Mallorca, SPAIN, NOV 05-07, 2007},
	pages = {21+},
	abstract = {Radial Basis Function Neural Networks (RBFN) have been recently studied due to their qualification as an universal function approximation. This paper investigates the use of RBF neural networks for software cost estimation, The focus of this study is on the design of these networks, especially their middle layer composed of receptive fields, using two clustering techniques: the C-means and the APC-III algorithms. A comparison between a RBFN using C-means and a RBFN using APC-III, in terms of estimates accuracy, is hence presented. This study uses the COCOMO{\textquoteright}81 dataset and data on Web applications from the Tukutuku database.},
	isbn = {978-3-540-85552-1},
	issn = {0302-9743},
	author = {Idri, Ali and Zahi, Azeddine and Mendes, Emilia and Zakrani, Abdelali},
	editor = {CuadradoGallego, JJ and Braungarten, R and Dumke, RR and Abran, A}
}
@conference { ISI:000255983000005,
	title = {Fuzzy radial basis function neural networks for web applications cost estimation},
	booktitle = {2007 INNOVATIONS IN INFORMATION TECHNOLOGIES, VOLS 1 AND 2},
	year = {2007},
	note = {4th International Conference on Innovations in Information Technology, Dubai, U ARAB EMIRATES, NOV 18-20, 2007},
	pages = {21+},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The Fuzzy Radial basis function Neural Networks (FRBFN) for software cost estimation is designed by integrating the principles of RBFN and the fuzzy C-means clustering algorithm. The architecture of the network is suitably modified at the hidden layer to realise a novel neural implementation of the fuzzy clustering algorithm. Fuzzy set-theoretic concepts are incorporated at the hidden layer, enabling the model to handle uncertain and imprecise data, which can improve greatly the accuracy of obtained estimates. MMRE and Pred are used as measures of prediction accuracy for this comparative study. The results show that an RBFN using fuzzy C-means performs better than an RBFN using hard C-means. This study uses data on web applications from the Tukutuku database.},
	isbn = {978-1-4244-1840-4},
	author = {Idri, Ali and Zakrani, Abdelali and Elkoutbi, Mohamed and Abran, Alain}
}
