A new generic neural network (NN) application -improving computational efficiency of certain processes in numerical environmental models – is considered. This approach can be used to accelerate the calculations and improve the accuracy of the parameterizations of several types of physical processes which generally require computations involving complex mathematical expressions, including differ...

It is shown that chiral perturbation theory (in its original form by Wein-berg) can describe N N scattering with positive as well as negative effective range. Some issues connected with unnaturally large N N 1 S 0 scattering length are discussed. The chiral perturbation theory approach to the low-energy purely pionic processes [1] has been generalised for processes involving an arbitrary number...

the aim of the present study was to find the best or optimum topology of six chicken populations, which were genotyped based on nine highly polymorphic microsatellite markers. to reach this goal, different genetic distances based on infinite allele model (iam), stepwise mutation model (smm) and drawing phylogentic trees on un-weighted pair-group method using arithmetic averages (upgma) method w...

In this paper, we present a review of various computational experiments concerning neural network (NN) models developed for regional employment forecasting. NNs are nowadays widely used in several fields because of their flexible specification structure. A series of NN experiments is presented in the paper, using two data sets on German NUTS-3 districts. Individual forecasts are computed by our...

k-nearest neighbors (k-NN), which is known to be a simple and efficient approach, is a non-parametric supervised classifier. It aims to determine the class label of an unknown sample by its k-nearest neighbors that are stored in a training set. The k-nearest neighbors are determined based on some distance functions. Although k-NN produces successful results, there have been some extensions for ...

It is well known that in general, the nearest neighbour rule (NN) has sample complexity that is exponential in the input space dimension d when only smoothness is assumed on the label posterior function. Here we consider NN on randomly projected data, and we show that, if the input domain has a small ”metric size”, then the sample complexity becomes exponential in the metric entropy integral of...

This research introduces the hybrid Multilayer feed forward Neural Network (NN) and the Maximum Likelihood (ML) technique into the problem of estimating a single component chirp signal parameters. The unknown parameters needed to be estimated are the chirp-rate, and the frequency parameters. NN was trained with several thousands noisy chirp signals as the NN inputs, where the chirp-rate and the...

One approach used to develop computer systems for natuidentifying phrases, e.g. is a noun phrase; and (3) ral language processing (NLP) is that of Artificial Neural Netidentifying the relationships among phrases, e.g. is works (NNs). Because of the large number of parameters a the agent of . This is similar to Jain (1991). NN has (e.g. network topology, learning algorit...

Nearest Neighbor (NN) searching is a challenging problem in data management and has been widely studied in data mining, pattern recognition and computational geometry. The goal of NN searching is efficiently reporting the nearest data to a given object as a query. In most of the studies both the data and query are assumed to be precise, however, due to the real applications of NN searching, suc...