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Deep learning has demonstrated tremendous success in variety of application domains in the past few years. This new field of machine learning has been growing rapidly and applied in most of the application domains with some new modalities of applications, which helps to open new opportunity. There are different methods have been proposed on different category of learning approaches, which inclu...

In this paper, we introduce a hybrid approach based on neural network and optimization teqnique to solve ordinary differential equation. In proposed model we use heyperbolic secont transformation function in hiden layer of neural network part and bfgs teqnique in optimization part. In comparison with existing similar neural networks proposed model provides solutions with high accuracy. Numerica...

We study the possibility to employ neural networks to simulate jet clustering procedures in high energy hadron-hadron collisions. We concentrate our analysis on the Fermilab Tevatron energy and on the k⊥ algorithm. We employ both supervised and unsupervised neural networks. In the first case we consider a multilayer feed-forward network trained by the backpropagation algorithm: our results show...

in this paper, we interpret a fuzzy differential equation by using the strongly generalized differentiability concept. utilizing the generalized characterization theorem. then a novel hybrid method based on learning algorithm of fuzzy neural network for the solution of differential equation with fuzzy initial value is presented. here neural network is considered as a part of large eld called n...

In this paper we discuss a method for semi-supervised training of CNNs. By using auto-encoders to extract features from unlabeled images, we can train CNNs to accurately classify images with only a small set of labeled images. We show our method’s results on a shallow CNN using the CIFAR-10 dataset, and some preliminary results on a VGG-16 network using the STL-10 dataset.

natural fire inflicting irreparable damage to rangelands and forest areas is cause changes in landscape ecology. the purpose of this research is comparison of artificial neural network (ann) and line regression (lr) models to predict of forest and rangelands fires to this end, the data consist fire burned area and fire were used weather data over a period of 7 years (2006-2012(.the result indic...

Artificial neural networks are intelligent systems that have successfully been used for prediction in different medical fields. In this study, the efficiency of a neural network for predicting the survival of patients with acute pancreatitis is compared with days-of-survival obtained from patients. A three- layer back-propagation neural network was developed for this purpose. Clinical data (e.g...