The authors have proposed a new automatic classification scheme based on the conjoint use of the multi-layer perceptron (MLP) neural network and an enhanced particle swarm optimisation (EPSO) algorithm for its trainin...
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The authors have proposed a new automatic classification scheme based on the conjoint use of the multi-layer perceptron (MLP) neural network and an enhanced particle swarm optimisation (EPSO) algorithm for its training. In this work, six predominant categories of heartbeats from MIT-BIH database are considered, which are: normal, premature ventricular contraction, atrial premature contraction, right bundle branch block, left bundle branch block and paced beats. First, the authors have applied the standard particle swarm optimisation (PSO) algorithm to select the network structure for each features vector. Then, the relevant electrocardiogram (ECG) features to the studied arrhythmias were chosen, which suited to the optimised training performance of the classifier. The recognition performance of the proposed EPSO-MLP classification system is evaluated considering two different versions of the EPSO algorithm. In the first version (EPSOw), the inertia weight factor of the PSO algorithm is proposed to be a variable with iterations. However, two PSO parameters are taken to be variables in the second version of the improved learning algorithm (EPSOwc). The obtained experimental results prove the enhancement of the convergence ability of the MLP neural network and confirm the superiority of the proposed EPSO-MLP classification scheme on comparison with the other last published classification systems.
The PV generation system is an uncontrollable source and has an affect on the grid by randomness of output power. So we need to strengthen the study of PV output power forecast. In this paper, according to the related...
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ISBN:
(纸本)9783037856345
The PV generation system is an uncontrollable source and has an affect on the grid by randomness of output power. So we need to strengthen the study of PV output power forecast. In this paper, according to the related information of recent day with the same weather type, an improved wavelet neural network forecasting model without solar radiation was proposed. Furthermore, with the measured data came from a PV power plant, comparison experiments were made as opposed to improved wavelet neural network forecasting model and the wavelet neural network forecasting model with traditional learningalgorithm. The experimental results indicate that the improved wavelet neural network forecasting model can significantly improve the precision of PV output power prediction. The comparison experiments considering solar radiation were also given, which also show the high precision and high efficiency of proposed model and algorithm.
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