in this paper, the crack detection and depth ratio estimation method are presented in beamlikestructures using elman networks. for this purpose, by using the frequencies of modes asinput, crack depth ratio of each element was detected as output. performance of the proposedmethod was evaluated by using three numerical scenarios of crack for fixed-simply supportedbeam consisting of a single crack...

In this paper, the application of multiple Elman neural networks to time series data regression problems is studied. An ensemble of Elman networks is formed by boosting to enhance the performance of the individual networks. A modified version of the AdaBoost algorithm is employed to integrate the predictions from multiple networks. Two benchmark time series data sets, i.e., the Sunspot and Box-...

in this paper, the gain in ld-celp speech coding algorithm is predicted using three neural models, that are equipped by genetic and particle swarm optimization (pso) algorithms to optimize the structure and parameters of neural networks. elman, multi-layer perceptron (mlp) and fuzzy artmap are the candidate neural models. the optimized number of nodes in the first and second hidden layers of el...

In this paper, the gain in LD-CELP speech coding algorithm is predicted using three neural models, that are equipped by genetic and particle swarm optimization (PSO) algorithms to optimize the structure and parameters of neural networks. Elman, multi-layer perceptron (MLP) and fuzzy ARTMAP are the candidate neural models. The optimized number of nodes in the first and second hidden layers of El...

The Vapnik Chervonenkis dimension of Elman networks is innnite. Here, we nd constructions leading to lower bounds for the fat shattering dimension that are linear resp. of order log 2 in the input length even in the case of limited weights and inputs. Since niteness of this magnitude is equivalent to learnability, there is no a priori guarantee for the generalization capability of Elman networks.

In this paper, the gain in LD-CELP speech coding algorithm is predicted using three neural models, that are equipped by genetic and particle swarm optimization (PSO) algorithms to optimize the structure and parameters of neural networks. Elman, multi-layer perceptron (MLP) and fuzzy ARTMAP are the candidate neural models. The optimized number of nodes in the first and second hidden layers of El...

Network latency is a crucial factor affecting the quality of communications networks due to irregularity vehicular traffic. To address problem performance degradation or instability caused by in networks, this paper proposes time delay prediction algorithm, which digital twin technology employed obtain large quantity actual data for and verify autocorrelation. Subsequently, meet conditions ARMA...

This paper investigates the application of wavelet transform as a preprocessor for neural networks (NN) in identifying internal turn-to-turn faults in transformer windings. The faulty and normal signals generated by numerical simulation of ElectroMagnetic Transient Program (EMTP) are preprocessed using discrete wavelet transform (DWT). The mean values of the wavelet coefficients are input into ...

This article presents an alternative approach useful for medical practitioners who wish to detect malaria and accurately identify the level of severity. Malaria classifiers are usually based on feed forward neural networks. In this study, the proposed classifier is developed based on the Jordan-Elman neural networks. Its performance is evaluated using a receiver-operating characteristic curve, ...