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This paper presents a Hybrid Multi-Layer Feedforward Neural Network (HMLFNN) technique for predicting the output from a Grid-Connected Photovoltaic (GCPV) system. In the proposed HMLFNN, Fast Evolutionary Programming (FEP) was employed to optimize the training process of the Multi-Layer Feedforward Neural Network (MLFNN). FEP was used to select the optimal values for the number of neurons in the hidden...
This paper proposes a novel method for predicting the amount of source code changes (changed lines of code: changed-LOC) in the open source development (OSD). While the software evolution can be observed through the public code repository in OSD, it is not easy to understand and predict the state of the whole development because of the huge amount of less-organized information.The method proposed...
The features of a short-term prediction of a stock price using a multi-layer perceptron in a moving simulation application mode are considered in this paper. The input data for the short-term prediction mode are analyzed. The architecture of the predicting model is developed. The simulation modeling results show a high accuracy of the prediction on the historical stock prices of Fiat company.
In this paper the feasibility of artificial neural network technology for air fine particles pollution prediction of main traffic route was discussed. The concentration data of PM2.5, PM5 and PM10 were measured in Zhongshan road, the main traffic route of Chongqing, China. Parameter Φ of emission capacity of motor vehicles was used as the independent variable of prediction model. RBF and BP neural...
A prediction method of coal and gas outburst was presented based on the combination of attribute reduction function of rough set theory and nonlinear mapping characteristics of support vector machine. Firstly, attribute reduction and denoising were executed. Secondly, the training samples that have been processed were input to the support vector machine to train the model. Finally, the trained model...
In order to predict the magnitude of the serious earthquake in future time in a seismic area, the probabilistic neutral network was established depending on mathematically computed parameters known as seismicity indicators. The indicators concerned are the time elapsed during a especial number (n) of critical seismic events before the day in question, the inclination of the Gutenberg_Richter inverse...
The prediction of chaotic time series is performed by least square support vector machine (LS-SVM) based on particle swarm optimization (PSO). The main objective of this approach is to increase the accuracy of the chaotic time series prediction. For the generation performance of LS-SVM depending on a good setting of its parameters, PSO is adopted to choose the global optimum parameters of LS-SVM automatically...
Groundwater level has random characters because of influences factors of natural and anthropogenic. Study random prediction model of groundwater level on the basis of groundwater physical process analysis is important to groundwater appraisal. The theory of supporting vector machine based on small-sample machine learning theory is introduced into dynamic prediction of groundwater level. A least square...
In this paper, a focused time lagged recurrent neural network (FTLRNN) model with gamma memory is developed for multi step ahead (k=1,5,10.20,50,100) prediction of typical Duffing Chaotic time series. It is popularized in Neural network field due to its richness in chaotic behavior. It is observed that duffing time series exhibit a rich chaotic behavior. This paper compares the performance of two...
Analysis of environmental data by means of Artificial Intelligence has become a quite active area of scientific research. Some techniques which have found important application in this area are neural networks, genetic algorithms, and other pattern classifier algorithms, such as SVMs. In the current paper, a member of the Associative Approach of pattern recognition of recent proposal is applied to...
Hybrid and adaptative system of gas concentration prediction in hard-coal mines is presented in the paper. The system widens functionality of the SMP-NT system which monitors gas concentration in mining excavations based on data collected from sensors. The SMP-NT system has also ability to automatic cut off electric energy in the case of explosion risk identification. A task of the prediction system...
In order to solve the problem of random and fluctuation of experiment errors and predication errors of neural network, a neural network model modified by a fuzzy Markov chain was introduced, When neural network was used to predict, the prediction errors between actual value and output value of the network were distributed randomly. That can be simulated by a Markov chain. According to the forecasting...
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