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This paper proposes an ANFIS based approach for one-day-ahead hourly wind power generation prediction. The increasing penetration of wind energy to electric power generation systems imposes important issues to address resulting from its intermittent and uncertain nature. These challenges necessitate an accurate wind power generation forecasting tool for planning efficient operation of power systems...
This paper deals with a hybrid GA-BP ANN approach for wind power prediction. Wind energy is one of the renewable energy options recently being developed significantly throughout the world in order to achieve low-emission targets, and it keeps extending its penetration in electric power generation. However, there are important issues emerging in the integration of wind power resulting from its intermittent...
In [1], a flexible and general control system architecture that allows for modelling, simulation and control of different models of maritime cranes and, more generally, robotic arms was previously presented by our research group. Each manipulator can be controlled by using the same universal input device regardless of differences in size, kinematic structure, degrees of freedom (DOFs), body morphology,...
The measurements for breakdown voltages have been made with point-plane electrodes for high-pressure carbon dioxide up to supercritical conditions at different temperatures. The breakdown voltages depend on electrode gap, temperature and pressure of gas while preserving the intrinsic nonlinear combination of these characteristics. Artificial neural network was used to model the complex nonlinear relationship...
The present paper proposes an new optimal Elman neural network modelling to predict the knitted fabric colour properties including the colour yield (in term of K/S value) and CIE Lab values of knitted fabrics under the effect of laser engraving process. The new modified Elman network which have a proportional (P), integral (I), and derivative (D) properties is introduced in this paper. The experiment...
Y/Δ transformer is widely used in power system and the circulation is difficult to measure accurately. This paper mainly discusses how to use the sample data to calculate the circulation of Y/Δ transformer. The method is deduced by the basic equations of transformer and the relationship of transformer currents. It uses numerical integration and trapezoid method to disperse the middle variables and...
In this paper, we describe a novel conversion method for voice conversion (VC). Artificial Neural Network (ANN) model is employed for performing joint spectrum and pitch conversion between speakers. The conventional method converts spectral parameters and pitch independently. Those separate transformations lead to an unsatisfactory speech quality. The main reason maybe that F0 sequences are usually...
In this paper a new knitted garment defect detection and classification model based on 2D Gabor wavelet transform and Elman neural network is introduced. A new modified Elman network is proposed to classify the type of fabric defects which have proportional (P), integral (I), derivative (D) properties. The proposed inspecting model in this study is more feasible and applicable in fabric defect detection...
In this paper, one fabric defect detection and classification system based on 2D Gabor wavelet transform and Elman neural network is introduced. In the proposed scheme, the texture features of the textile fabric are extracted by using an optimal 2D Gabor filter. A new modified Elman network is proposed to classify the type of fabric defects which have a proportional (P), integral (I) and derivative...
Cognitive radio that supports a secondary and opportunistic access to licensed spectrum shows great potential to dramatically improve spectrum utilization. Spectrum sensing performed by secondary users to detect unoccupied spectrum bands, is a key enabling technique for cognitive radio. This paper proposes a truncated sequential spectrum sensing scheme, namely the sequential shifted chi-square test...
This study proposes a hybrid model which combines the linear autoregression (AR) with the nonlinear neural network (NN) based on the extreme learning machine (ELM) in an integral structure in order to improve the accuracy of time-series prediction. Unlike the developed hybrid forecasting models introduced in the literature, which usually treat the original forecasting models as a separate linear or...
This paper deals with the problem of time-varying (TV) channel estimation for multiple-input multiple-output/orthogonal frequency-division multiplexing (MIMO/OFDM) systems based on superimposed training (ST). The time-varying coefficients of the TV channel are firstly modeled by truncated discrete Fourier bases, and then optimally estimated both in one OFDM symbols and over multiple OFDM symbols by...
In this paper, we proposed a kernel difference-weighted k-nearest neighbor classifier (KDF-WKNN) for the diagnosis of cardiac arrhythmia based on the standard 12 lead ECG recordings. Different from classical KNN, KDF-WKNN defines the weighted KNN rule as the constrained least-squares optimization of sample reconstruction from its neighborhood, and then uses the Lagrangian multiplier method to compute...
The idea of this research is to determine can we tell from the HRV data without paroxysmal atrial fibrillation present at the recording if the patient suffers from this arrhythmia. The benefit is we can provide time and cost effective preliminary screening procedure during short time visit to the clinic. To achieve this goal we used Fourier analysis of the 30 minute HRV segment duration. We found...
The idea of this research is to determine how long in advance can we predict the onset of paroxysmal atrial fibrillation (PAF) from the HRV data. Having established such methods with great confidence it is possible to avert the PAF onset using pacing techniques, thus reliving patient pains.
This paper presents a new learning methodology for feedforward neural networks. The proposed algorithm is based on Davidon's Lea-square minimization approach. We modified the original Davidon algorithm so that it can handle more than one error term at each iteration. This increases the cost of computation but a trade-off can be achieved between the increase in cost and the improvement in accuracy...
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