this paper intends to offer a new iterative method based on arti cial neural networks for finding solution of a fuzzy equations system. our proposed fuzzi ed neural network is a ve-layer feedback neural network that corresponding connection weights to output layer are fuzzy numbers. this architecture of arti cial neural networks, can get a real input vector and calculates its corresponding fu...

-This paper focuses the function approximation capability of feed forward neural network (FFNN). A Graphical user Interface (GUI) system has been developed and tested for function approximation. This GUI system can approximate any nonlinear/linear function which can have any number of input variable and six output variables. Configuration of neural network can be set from a single GUI window. A...

Reclaimed asphalt pavement (RAP) is one of the waste materials that highway agencies promote to use in new construction or rehabilitation of highways pavement. Since the use of RAP can affect the resilient modulus and other structural properties of flexible pavement layers, this paper aims to employ two different artificial neural network (ANN) models for modeling and evaluating the effects of ...

A feed forward neural network (FFNN) has been trained to discriminate between power transformer magnetizing inrush and fault currents. The training algorithm used was back-propagation, assuming initially a sigmoid transfer function for the network’s processing units (“neurons”). Once the network was trained the units’ transfer function was changed to hard limiters with thresholds equal to the b...

The purpose of this paper is to develop an appropriate artificial neural network (ANN) model of induction motor bearing (IMB) failure prediction. Acoustic emission (AE) represented the technique of collecting the data that was collected from the IMB and this data were measured in term of decibel (dB) and Distress level. The data was then used to develop the model using ANN for IMB failure predi...

In the dynamic global economy, the accuracy in forecasting the foreign currency exchange rates is of crucial importance for any future investment. The use of computational intelligence based techniques for forecasting has been proved extremely successful in recent times. The aim of this study is to identify a neural network model which has ability to predict the US Dollar against Sri Lankan Rup...

The paper is based on feed forward neural network (FFNN) optimization by particle swarm intelligence (PSI) used to provide initial weights and biases to train neural network. Once the weights and biases are found using Particle swarm optimization (PSO) with neural network used as training algorithm for specified epoch, the same are used to train the neural network for training and classificatio...

Recurrent neural network (RNN) are being extensively used over feed-forward neural networks (FFNN) because of their inherent capability to capture temporal relationships that exist in the sequential data such as speech. This aspect of RNN is advantageous especially when there is no a priori knowledge about the temporal correlations within the data. However, RNNs require large amount of data to ...

t is becoming increasingly difficult to have data security nowadays. There have been used various cryptography methods in literature, but recent developments in computational area have heightened the need of new methods. In this study the feed-forward artificial neural network (FFNN) was used with a different perspective by using the structure of artificial neural network as a key as a solution...

This letter describes the first derivatives estimation of nonlinear parameters through an embedded identifier in the hybrid system by using a feed-forward neural network (FFNN). The hybrid systems are modelled by the differential-algebraic-impulsive-switched (DAIS) structure. The FFNN is used to identify the full dynamics of the hybrid system. Moreover, the partial derivatives of an objective f...