Surface classification using ANN and complex-valued neural network

Complex variable based backpropagation algorithm (CVBP) is a new development in neural networks (ANN). The new tool of approximation is designed to train complex-variable based neural networks (CNN) in which the weights, functions of activation are complex in nature. The CVBP also is developed over a quadratic error function (same as the backpropagation algorithm). The present paper explores the possibility of using different Error Functions and compares the performance of each of them (ANN and CNN over Error Functions) by applying to a surface classification problem.