نتایج جستجو برای: interval prediction neural networks
تعداد نتایج: 1045591 فیلتر نتایج به سال:
Background: Pregnancy in women with systemic lupus erythematosus (SLE) is still introduced as a major challenge. Consulting before pregnancy in these patients is essential in order to estimating the risk of undesirable maternal and fetal outcomes by using appropriate information. The purpose of this study was to develop an artificial neural network for prediction of pregnancy outcomes including...
In this paper we discuss on a system where in automatically the images collected from the spacecraft are identified for the connected images. the satellite images taken consist of not only basic shapes with regular size and shape but also the interconnected irregular shapes which is identified as network. Now the images that are automatically classified are either normal weather patterns or adv...
With respect to the fact that the prediction of Protein secondary structure based on amino acids is very important, therefore, this study tries to present a new method based on the fuzzy combinational structure of a set of feed-forward neural networks so that the prediction accuracy of Protein secondary structure can be improved compared with the existing methods. Neural networks used in this p...
As one of the important energy forms, natural gas consumption has an upward trend in recent years. Therefore management and planning for provision of it requires prediction of the future consumption. But many of prediction procedures are inherently stochastic therefore it is important to have better knowledge about the robustness of prediction procedures. This paper compares robustness of two p...
For decades, computational intelligence techniques have been developed and applied to many real world problems. In this paper, tree architectures of fuzzy neural networks are applied to Auto MPG prediction problem. The dataset concerns city-cycle fuel consumption in miles per gallon, to be predicted in terms of 3 multivalued discrete and 5 continuous attributes. Tree architectures of fuzzy neur...
The Multi-Layered Perceptron (MLP) Neural networks have been very successful in a number of signal processing applications. In this work we have studied the possibilities and the met difficulties in the application of the MLP neural networks for the prediction of daily solar radiation data. We have used the PolackRibière algorithm for training the neural networks. A comparison, in term of the s...
The use of third-party logistics (3PL) providers is regarded as new strategy in logistics management. The relationships by considering 3PL are sometimes more complicated than any classical logistics supplier relationships. These relationships have taken into account as a well-known way to highlight organizations' flexibilities to regard rapidly uncertain market conditions, follow core competenc...
In this paper, we present a general framework for understanding the role of arti®cial neural networks (ANNs) in bankruptcy prediction. We give a comprehensive review of neural network applications in this area and illustrate the link between neural networks and traditional Bayesian classi®cation theory. The method of cross-validation is used to examine the between-sample variation of neural net...
energy management is one of the main ways of the efficient use of energy resources. the prediction of crop yields based on energy inputs can help farmers and policymakers to estimate the level of production. required data for study were randomly collected from 70 broiler farms in north west of iran. the input energies were included human labour, machinery, fuel, feed and electricity and the out...
Generalized correlation higher order neural network designs are developed. Their performance is compared with that of first order networks, conventional higher order neural network designs, and higher order linear regression networks for financial time series prediction. The correlation higher order neural network design is shown to give the highest accuracy for prediction of stock market share...
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