A complex valued neural network is a neural network, which consists of complex valued input and/or weights and/or thresh olds 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 visio...

estimate of sediment load is required in a wide spectrum of water resources engineering problems. the nonlinear nature of suspended sediment load series necessitates the utilization of nonlinear methods to simulate the suspended sediment load. in this study artificial neural networks (anns) are employed to estimate daily suspended sediment load. two different ann algorithms, multi layer percept...

As the size of samples grows, the accuracy of trained multi-layer perceptrons grows with some improvement in error rates. But we cannot use larger and larger samples, because computational complexity to train the multi-layer perceptrons becomes enormous and data overfitting problem can happen. This paper suggests an effective approach in determining a proper sample size for multi-layer perceptr...

In this paper we evaluate the classiication accuracy of four statistical and three neural network classiiers for two image based pattern classiication problems. These are ngerprint classiication and optical character recognition (OCR) for isolated handprinted digits. The evaluation results reported here should be useful for designers of practical systems for these two important commercial appli...

Since its emergence from the early 1980s, the field of Human Computer Interaction has moved on and advanced in many significant ways. It has opened up a world in which communication between human and computer has become easier and richer. Among the different modes of interaction, Gestures provide the most natural and convenient way of communication. Hence gesture recognition has been extensivel...

In this research, Artificial Neural Networks (ANNs) have been used as a powerful tool to solve the inverse kinematic equations of a parallel robot. For this purpose, we have developed the kinematic equations of a Tricept parallel kinematic mechanism with two rotational and one translational degrees of freedom (DoF). Using the analytical method, the inverse kinematic equations are solved for spe...

We examine the e ect of constraining the number of hidden units For one hidden layer networks with fairly general type of units including perceptrons with any bounded activation function and radial basis function units we show that when also the size of parameters is bounded the best approximation property is satis ed which means that there always exists a parameterization achieving the global ...

| Adaptive methods for performing multiuser demodula-tion in a Direct-Sequence Spread-Spectrum Multiple-Access (DS/SSMA) communication environment are investigated. In this scenario, the noise is characterized as being the sum of the interfering users' signals and additive Gaussian noise. The optimal receiver for DS/SSMA systems has a complexity that is exponential in the number of users. This ...

This paper introduces the Petri Net Radial Basis Function Perceptron (PNRBFP), a modified Petri Net that exhibits behavior equivalent to that of a typical radial basis function Perceptron when used in neural networking applications under certain domain restrictions. The PNRBFP makes use of modified transitions to perform basis function calculations and 'fuzzy' style tokens to transport values o...