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...

In this paper, we compare the forecasting accuracy of two neural network models in forecasting earnings per share of Chinese listed companies based upon fundamental accounting variables. In one neural network model, weights estimated by back propagation were utilised, and in the other model a genetic algorithm was utilised. Based upon a sample of 723 Chinese companies in 22 industries over a te...

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...

This paper is proposed the face recognition method using PCA with neural network back error propagation learning algorithm .In this paper a feature is extracted using principal component analysis and then classification by creation of back propagation neural network. We run our algorithm for face recognition application using principal component analysis, neural network and also calculate its p...

In this paper, we propose an approach to adaptive neural network control by using a new adaptation algorithm. The algorithm is derived from the theory of adaptive interaction. The principle behind the adaptation algorithm is a simple but efficient methodology to perform gradient descent optimization in the parametric space. Unlike the approach based on the back-propagation algorithm, this appro...

We use simulated soccer to study multi-agent learning. Each team member tries to learn from the corresponding human player in a real game. Following a uniied approach, strategic and tactical behavior is learned synergistically by training a feed-forward neural network (ANN) with a mod-iied back-propagation algorithm. It aims at decreasing the learning time and avoiding the local maximums. We tr...

the spatial distribution of petrophysical properties within the reservoirs is one of the most importantfactors in reservoir characterization. flow units are the continuous body over a specific reservoirvolume within which the geological and petrophysical properties are the same. accordingly, anaccurate prediction of flow units is a major task to achieve a reliable petrophysical description of a...

We describe a two-stage approach to the use of pixel based neural networks for object detection problems in which the locations of relatively small objects in large pictures must be found. The networks use a squared input eld which is large enough to contain all objects of interest. In the rst stage the network is trained on examples which have been cut out from the large pictures. A back error...

In this paper, vapor pressure for pure compounds is estimated using the Artificial Neural Networks and a simple Group Contribution Method (ANN–GCM). For model comprehensiveness, materials were chosen from various families. Most of materials are from 12 families. Vapor pressure data of 100 compounds is used to train, validate and test the ANN-GCM model. Va...

crack identification is a very important issue in mechanical systems, because it is a damage that if develops may cause catastrophic failure. in the first part of this research, modal analysis of a multi-cracked variable cross-section beam is done using finite element method. then, the obtained results are validated usingthe results of experimental modal analysis tests. in the next part, a nove...