TY - JFULL AU - Anupama Pande and Vishik Goel PY - 2007/3/ TI - Complex-Valued Neural Network in Image Recognition: A Study on the Effectiveness of Radial Basis Function T2 - International Journal of Computer and Information Engineering SP - 344 EP - 350 VL - 1 SN - 1307-6892 UR - https://publications.waset.org/pdf/6242 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 2, 2007 N2 - A complex valued neural network is a neural network, which consists of complex valued input and/or weights and/or thresholds and/or activation functions. Complex-valued neural networks have been widening the scope of applications not only in electronics and informatics, but also in social systems. One of the most important applications of the complex valued neural network is in image and vision processing. In Neural networks, radial basis functions are often used for interpolation in multidimensional space. A Radial Basis function is a function, which has built into it a distance criterion with respect to a centre. Radial basis functions have often been applied in the area of neural networks where they may be used as a replacement for the sigmoid hidden layer transfer characteristic in multi-layer perceptron. This paper aims to present exhaustive results of using RBF units in a complex-valued neural network model that uses the back-propagation algorithm (called 'Complex-BP') for learning. Our experiments results demonstrate the effectiveness of a Radial basis function in a complex valued neural network in image recognition over a real valued neural network. We have studied and stated various observations like effect of learning rates, ranges of the initial weights randomly selected, error functions used and number of iterations for the convergence of error on a neural network model with RBF units. Some inherent properties of this complex back propagation algorithm are also studied and discussed. ER -