Proper

Abstract

Last two decades have witnessed tremendous work in the field of Neurocomputing. A neural network is a massively parallel distributed processor made up of simple processing units which has a natural property of acquiring knowledge through a learning process and storing this experimental knowledge in synaptic weights so that it is available for future use. Researchers from many scientific disciplines are designing Artificial Neural Networks (Single / Multilayer) to solve a variety of problems in pattern recognition, prediction, optimization, associative memory, control and classifier as they turned out to be very powerful tool for almost any type of applications. Traditional Neural Networks’ parameters are usually real numbers for dealing with real-valued data. However, high-dimensional data also appear in practical applications and consequently, high-dimensional neural networks have been proposed (like CVNN). They have also presented improved results even in case of real-valued problems. Due to their diversity and abundance it is now becoming difficult to represent Neural Networks in complex domain consequently they started facing representational problems.