Image compression technique is used to reduce the number of its required in representing image, which helps to reduce the storage space and transmission cost. In the present research work back propagation neural network training algorithm has been used. Back propagation neural network algorithm helps to increase the performance of the system and to decrease the convergence time for the training...

As traditional neural network consumes a significant amount of computing resources during back propagation, Sun et al. (2017) propose a simple yet effective technique to alleviate this problem. In this technique, only a small subset of the full gradients are computed to update the model parameters. In this paper we extend this technique into the Convolutional Neural Network(CNN) to reduce calcu...

Short term load forecasting (STLF) plays an important role in the economic and reliable operation ofpower systems. Electric load demand has a complex profile with many multivariable and nonlineardependencies. In this study, recurrent neural network (RNN) architecture is presented for STLF. Theproposed model is capable of forecasting next 24-hour load profile. The main feature in this networkis ...

In this study, colorimetric calibration of scanner has been done via perceptron neural network with three or four layers by back propagation algorithm for colored polyester fabrics. The results obtained for random training samples are not satisfactory but application of selective training samples for L*a*b* or RGB leads to good results, with better results obtained for the L*a*b* method. On the...

the artificial immune system applications vary from anomaly detection, fault tolerance, as well as data classification and data clustering. It was noticed that the applications on the design of the artificial immune memory are sparse, despite its importance in the learning process within the artificial immune networks. Most of the work presented focused only on the secondary immune response. In...

The back-propagation neural network algorithm is a commonly used method for predicting the secondary structure of proteins. Whilst popular, this method can be slow to learn and here we compare it with an alternative: the cascade-correlation architecture. Using a constructive algorithm, cascade-correlation achieves predictive accuracies comparable to those obtained by back-propagation, in shorte...

A Back Propagation Artificial Neural Network (BPANN) is a well-known learning algorithmpredicated on a gradient descent method that minimizes the square error involving the networkoutput and the goal of output values. In this study, 261 GPS/Leveling and 8869 gravity intensityvalues of Iran were selected, then the geoid with three methods “ellipsoidal stokes integral”,“BPANN”, and “collocation” ...

This study presents a new procedure based on Artificial Neural Network (ANN) for identification of double cracks in Functionally Graded Beams (FGBs). A cantilever beam is modeled using Finite Element Method (FEM) for analyzing a double-cracked FGB and evaluation of its first four natural frequencies for different cracks depths and locations. The obtained FEM results are verified against availab...

The aim of this research is to develop a farmer prediction system to identify crop suitable for particular soil. To achieve this Neural Network should be trained to perform correct prediction for farmers. After the network has been properly trained, it can be used to identify the crop suitable for particular type of soil. The Artificial neural networks are relatively crude electronic networks o...

a neural network is developed for the determination of leaky confined aquifer parameters. leakage into the aquifer takes place from the storage in the confining aquitard. the network is trained for the well function of leaky confined aquifers by the back propagation technique and adopting the levenberg–marquardt optimization algorithm. by applying the principal component analysis (pca) on the a...