In the context of noncommutative space-time we investigate the nucleon structure functions which play an important role in identifying the internal structure of nucleons. We use the corrected vertices and employ new vertices that appear in two approaches of noncommutativity and calculate the proton structure functions in terms of the noncommutative tensor θ μν . To check our results we plot the nucleon structure function (NSF), F 2 (x), and compare it with experimental data and the results from the GRV, GJR and CT10 parametrization models. We show that with the new vertex that arises the noncommutativity correction will lead to a better consistency between theoretical results and experimental data for the NSF. This consistency will be better for small values of the Bjorken variable x. To indicate and confirm the validity of our calculations we also act conversely. We obtain a lower bound for the numerical values of NC scale which correspond to recent reports.
In this paper, we present an orthonormal version of the new information criterion (NIC) algorithm for fast estimation and tracking of signal subspace using a twolayer linear neural network (NN). The fast orthonormal NIC referred as to FONIC algorithm guarantees the orthonormality of the weight matrix at each iteration. The proposed FONIC algorithm has a linear complexity which makes it efficient in real time applications. The FONIC algorithm provides a fast on-line learning of the optimum weights for the two-layer linear. Simulation results show better performance of FONIC algorithm than the NIC algorithm.Index Terms-new information criterion, subspace tracking, adaptive algorithm, neural network learning.I.
Electric and magnetic moment distributions are presenting by form factors (FF)s. Noncommutative space-time (NCST) includes an additional Lorentz index which are effecting on FFs. In this content we investigate electron-proton elastic scattering to impose the noncommutative effect on FFs and to obtain their physical meaning. Two Rosenbluth and polarization methods are utilized in NCST. The second method is not affected by NCST. When we resort to polarization method, the ratio of electric form factor to magnetic form factor in NCST is identical to the one in normal space time. This indicates the priority of polarization method to measure experimentally the concerned ratio as is expecting. On the other hand, the pure NC effect makes to appear an extra ratio, denoted by $$R_{NC}$$ R NC . If we let the variation of this quantity to cover the difference between the experimental results for Resonbluth and polarization ratio then the accepted lower limit of $$\Lambda _{NC}$$ Λ NC as NC scale is achieved which is corresponding to 180$$^\circ $$ ∘ for the scattering angle.
The use of Probabilistic Neural Network (PNN) is very common in supervised pattern recognition applications. PNN is based on Bayes decision rule and it usesGaussian Parzen windows for estimating the probability density functions (pdf) required in Bayes rule. The conventional PNN needs a single spread value for pdf estimation which is proportional to Gaussian window width. In this paper we will suggest the use of a multispread PNN structure whose spread values are estimated using the training data. In addition, we will introduce several new discriminating features of acoustic radiated noise which can be used for ship noise classification. These features will be used as discriminating features in the conventional and multi-spread PNN. Finally, the performance of the conventional PNN and the suggested multi-spread PNN in classifying real ship noise data will be compared. Results of this comparison show that the performance of the multi-spread PNN is better than the conventional PNN.
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