In most countries, the main step in the process of power system restoration, following a complete/partial blackout, is energization of primary restorative transmission lines. Artificial neural network (ANN) is employed for performing a nonlinear input–output mapping in this work, in order to estimate the temporary overvoltages (TOVs) due to transmission lines energization. In the proposed metho...

Hairpin bandpass filter are compact structures they may theoretically be obtained by folding the resonator of parallel-coupled half wave length resonator which reduces the coupling between resonators. This type of U shape resonator is so called hair pin resonator. In the present paper a novel technique has been proposed for the estimation of bandwidth for variation of slot length on the bandpas...

have aroused great interest in fi elds as diverse as biology, psychology, medicine, economics, mathematics, statistics and computer science. The main reason underlying this interest lies in the fact that ANN are general, fl exible, nonlinear tools capable of approximating any sort of arbitrary function (Hornik, Stinchcombe, & White, 1989). Due to their fl exibility as function approximators, AN...

In this paper we present our teamwork main results in the area of the automatic speech recognition and understanding in Romanian language using hidden Markov models (HMM), artificial neural networks (multilayer perceptron, support vector machines, Kohonen networks) and hybrid models (fuzzy HMM, fuzzy multilayer perceptron, HMM/multilayer perceptron) for different small Romanian language corpora...

In this study, artificial neural network was used to predict the microhardness of Al2024-multiwall carbon nanotube(MWCNT) composite prepared by mechanical alloying. Accordingly, the operational condition, i.e., the amount of reinforcement, ball to powder weight ratio, compaction pressure, milling time, time and temperature of sintering as well as vial speed were selected as independent input an...

The paper describes an artificial neural network (ANN) model to predict the height of destressed zone (HDZ) which is taken as equivalent to the combined height of caved and fractured zones above the mined panel in longwall mining. For this, the suitable datasets have been collected from the literatures and prepared for modeling. The data were used to construct a multilayer perceptron (MLP) netw...

In this paper we propose and analyze a multilayer perceptron-like model with matrix inputs. We applied the proposed model to the financial time series prediction problem, compared it with the standard multilayer perceptron model, and got fairly good results.

This paper presents artificial neural network (ANN)-based models for forecasting precipitation, in which the training parameters are adjusted using a parameter automatic calibration (PAC) approach. A classical ANN-based model, the multilayer perceptron (MLP) neural network, was used to verify the utility of the proposed ANN–PAC approach. The MLP-based ANN used the learning rate, momentum, and n...

In this paper, simulated annealing guided traingularized encryption using multilayer perceptron generated session key (SATMLP) has been proposed for secured wireless communication. Both sender and receiver station uses identical multilayer perceptron and depending on the final output of the both side multilayer perceptron, weights vector of hidden layer get tuned in both ends. After this tunnin...

The paper describes the implementation of a systolic array for a multilayer perceptron on different FPGA architectures with a hardware-friendly learning algorithm: Pipelined On-line Backpropagation. By exploiting the embedded memories of certain families alongside the projection used in the systolic architecture, we can implement a very large interconnection layers. These physics and architectu...