cox regression model serves as a statistical method for analyzing the survival data, which requires some options such as hazard proportionality. in recent decades, artificial neural network model has been increasingly applied to predict survival data. this research was conducted to compare cox regression and artificial neural network models in prediction of kidney transplant survival. the prese...

In this study, artificial neural network was used to predict the surface tension of 20 hydrocarbon mixtures. Experimental data was divided into two parts (70% for training and 30% for testing). Optimal configuration of the network was obtained with minimization of prediction error on testing data. The accuracy of our proposed model was compared with four well-known empirical equations. The arti...

The Cellular Neural Network (CNN) is an artificial neural network of the nearest neighbour interaction type. It has been used for image processing, pattern recognition, moving object detection, signal processing, augmented reality and etc. The cellular neural network CMOS array was implemented by Anguita et al [1 – 5] and Dalla Betta et al [6]. The design of a cellular neural network template i...

Most of the existing SICNN models are concerns with the delays in the state. However, it is not enough for it can not describe the phenomenon precisely. It is natural and useful to consider the model with a neutral delay, it means that the system describing the model depends on not only the past information of the state but also the information of the derivative of the state, i.e., the decay ra...

The Cellular Neural Network (CNN) is an artificial neural network of the nearest neighbour interaction type. It has been used for image processing, pattern recognition, moving object detection, signal processing, augmented reality and etc. The cellular neural network CMOS array was implemented by Anguita et al [1 5] and Dalla Betta et al [6]. The design of a cellular neural network template is ...

in the near future the use of distributed generation systems will play a big role in the production ofelectrical energy. one of the most common types of dg technologies , fuel cells , which can be connectedto the national grid by power electronic converters or work alone studies the dynamic behavior andstability of the power grid is of crucial importance. these studies need to know the exact mo...

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...

â â â â â â â  this paper proposes a new forecasting model for investigating relationship between the price of crude oil, as an important energy source and gdp of the us, as the largest oil consumer, and the uk, as the oil producer. gmdh neural network and mlff neural network approaches, which are both non-linear models, are employed to forecast gdp responses to the oil price changes. the resul...

modelling and forecasting stock market is a challenging task for economists and engineers since it has a dynamic structure and nonlinear characteristic. this nonlinearity affects the efficiency of the price characteristics. using an artificial neural network (ann) is a proper way to model this nonlinearity and it has been used successfully in one-step-ahead and multi-step-ahead prediction of di...

bedload transport is an essential component of river dynamics and estimation of its rate is important to many aspects of river management. in this study, measured bedload by helley- smith sampler was used to estimate the bedload transport of kurau river in malaysia. an artificial neural network, genetic programming and a combination of genetic programming and a neural network were used to estim...