Learning by optimization in random neural networks

Date

1998-10-28

Author

Atalay, Mehmet Volkan

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

162
views

0
downloads

The random neural network model proposed by Gelenbe has a number of interesting features in addition to a well established theory. Gelenbe has also developed a learning algorithm for the recurrent random network model using gradient descent of a quadratic error function. We present a quadratic optimization approach for learning in the random neural network, particularly for image texture reconstruction.

URI

https://hdl.handle.net/11511/55589

Conference Name

13th International Symposium on Computer and Information Sciences (ISCIS 98)

Collections

Department of Computer Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Fuzzy neural network learning method for time series analysis using multivariate autoregression Sisman, NA; Alpaslan, Ferda Nur (1998-11-13) This paper describes how temporal fuzzy neural network model proposed in [4] can be applied to time series analysis when a multivariate autoregressive model is constructed. The fuzzy multivariate autoregression procedure is described first, then the temporal fuzzy neural network model using this procedure is presented.
Exact stochastic simulation algorithms and impulses in biological systems Altıntan, Derya; Purutçuoğlu Gazi, Vilda (2018-01-01) The stochastic model is the only sort of expressions which can capture the randomness of biological systems under different reactions. There are mainly three methods; Gillespie, first reaction and next reaction algorithms; for implementing exact stochastic simulations in these systems. Although these algorithms are successful in explaining the natural behaviors of the systems’ activation, they cannot describe the absurd changes, i.e., impulses. Moreover, the source codes in R are not available and open fo...
Artificial-neural-network prediction of hexagonal lattice parameters for non-stoichiometric apatites Kockan, Umit; Ozturk, Fahrettin; Evis, Zafer (2014-01-01) In this study, hexagonal lattice parameters (a and c) and unit-cell volumes of non-stoichiometric apatites of M-10(TO4)(6)X-2 are predicted from their ionic radii with artificial neural networks. A multilayer-perceptron network is used for training. The results indicate that the Bayesian regularization method with four neurons in the hidden layer with a tansig activation function and one neuron in the output layer with a purelin function gives the best results. It is found that the errors for the predicted ...
Loop-based conic multivariate adaptive regression splines is a novel method for advanced construction of complex biological networks Ayyıldız Demirci, Ezgi; Purutçuoğlu Gazi, Vilda; Weber, Gerhard Wilhelm (2018-11-01) The Gaussian Graphical Model (GGM) and its Bayesian alternative, called, the Gaussian copula graphical model (GCGM) are two widely used approaches to construct the undirected networks of biological systems. They define the interactions between species by using the conditional dependencies of the multivariate normality assumption. However, when the system's dimension is high, the performance of the model becomes computationally demanding, and, particularly, the accuracy of GGM decreases when the observations...
Neural network based orbit prediction for a geostationary satellite Kutay, Ali Türker; Tulunay, Ersin; Tekinalp, Ozan (null; 2001-05-23) An artificial Neural Network (NN) model was developed to estimate the semi-major axis (a), the eccentricity (e) and the inclination (i) of a geostationary satellite orbit. To facilitate a comparison between the NN model developed herewith and a real case, the TORKSAT lB geostationary satellite has been taken as example. A code that numerically solves the parameters of the TORKSAT's orbit, namely METUAEE1, is used to generate the training data for the NN model and to evaluate its performance. A Multi-La...

Citation Formats

M. V. Atalay, “Learning by optimization in random neural networks,” Belek Antalya, TURKEY, 1998, vol. 53, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/55589.