In this paper a nonparametric neural network (NN) technique for prediction of future values of a signal based on its past history is presented. This approach bypasses modeling, identification, and parameter estimation phases that are required by conventional parametric techniques. A multi-layer feed forward NN is employed. It develops an internal model of the signal through a training operation...

Since past few years, researchers have been concentrating on the classification of Electromyography Signal. This method is very useful in diagnosing the neuro-muscular disorders, which consists of wide spread diseases affecting peripheral nervous system. Progressive muscle weakness is the major form of these disorders. Out of various proposed methods, scholars are commonly focusing on Neural Ne...

Forecasting a hydrologic time series has been one of the most complicated tasks owing to the wide range of data, the uncertainties in the parameters influencing the time series and also due to the non availability of adequate data. Recently Artificial Neural Networks (ANN) have become quite popular in time series forecasting in various fields. This paper demonstrates the use of ANN to forecast ...

This paper investigates the ability of several alternate models of Artificial Neural Network (ANN) to predict the probability of occurrence of Hypertension (HT) in a mixed patient population. To do this a two-layer feed-forward neural network with 13 inputs and 1 output was created with a single hidden layer . Different types of networks structures, such as NEWFF (feed-forward back propagation ...

In this paper a novel technique is proposed for the estimation of resonant frequency of coaxial feed equilateral triangular microstrip patch antenna. The major advantage of the proposed approach is that, after proper training, proposed neural model completely bypasses the repeated use of complex i terative process for calculation of resonant frequency, thus resulting in an extremely fast soluti...

Neural networks are a powerful tool used to model properties and behaviour of materials in many areas of civil engineering applications. In the present paper, the models in artificial neural networks for predicting the temperatures in timber under fire loading have been developed. For building these models, training and testing using the available numerical results obtained using design methods...

introduction: with using multiple linear regression (mlr), can simultaneously analyses several different variables, but to get the desirable results from the mlr, the samples must be much and accurate. therefore, this method has high sensitivity and may cause errors in results. in addition, to use this method, the variable must have normal distribution and modification follow from a linear rela...

Facial expression recognition is also gaining interest among the researchers because of its inevitable advantages in image retrieval which can be extended many fields like medicine, artificial intelligence, robotics and neural networks. So it is one of the hot topic for researchers. Existing methods such as PCA, LDA, LPP etc. with Euclidian distance classifier are popular. Neural network classi...

This paper proposes the neural network solution to the indirect vector control of three phase induction motor including an adaptive neuro fuzzy controller. The basic equations and elements of the indirect vector control scheme are given. The proposed control scheme is realized by an adaptive neuro-fuzzy controller and two feed forward neural network. The neuro-fuzzy controller incorporates fuzz...

In this study, the method to apply the feed forward neural networks with two different numbers of hidden layers for harmonic detection process in active filter are described. We have simulated the distorted wave including 5th, 7th, 11th, 13th harmonics and used them for training of the neural networks. The distorted wave including up to 25th harmonics were prepared for testing of the neural net...