Opt Quant Electron (2008) 40:891–905 DOI 10.1007/s11082-009-9290-5 Accurate radial basis function based neural network approach for analysis of photonic crystal fibers M. F. O. Hameed · S. S. A. Obayya · K. Al-Begain · A. M. Nasr · M. I. Abo el Maaty Received: 5 September 2008 / Accepted: 12 March 2009 / Published online: 9 April 2009 © Springer Science+Business Media, LLC. 2009 Abstract In this paper, a new and an accurate artificial neural network approach (ANN) is presented for the analysis and design of photonic crystal fibers (PCFs). The new ANN approach is based on the radial basis functions which offer a very quick convergence and high efficiency during the ANN learning. The accuracy of the suggested approach is demon- strated via the excellent agreement between the results obtained using the presented approach and the results of the full vectorial finite difference method (FVFDM). In addition, a new design of highly birefringence PCF with low losses for the two polarized modes is presented using the proposed approach. Keywords Photonic crystal fibers · Optimization · Artificial neural network · Radial basis function 1 Introduction Due to their unusual optical properties, photonic crystal fibers (PCFs) (Benabid 2006; Broeng et al. 1999) have attracted the interest of many researchers in recent years. PCFs can be endlessly single mode over a wide wavelength range (Birks et al. 1997), have a large effective mode area (Knight et al. 1998) and can be tailored to achieve nearly zero and flat dispersion over a wide range of wavelengths (Gander et al. 1998). PCFs are usually made of a silica background which contains a regular array of air holes running through the length of the fibre acting as a cladding. This structure creates bandgaps where propagation at certain optical frequencies is forbidden. If the central hole is enlarged (low index core) (Benabid 2006) or removed (high index core) (Broeng et al. 1999), a defect will be produced in the periodic structure. High and low index core PCFs are very promising structures in terms M. F. O. Hameed · S. Obayya (B ) · K. Al-Begain Faculty of Advanced Technology, University of Glamorgan, Pontypridd, CF37 1DL, UK e-mail: sobayya@glam.ac.uk A. M. Nasr · M. I. Abo el Maaty Faculty of Engineering, University of Mansoura, Mansoura, Egypt 123