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Aerosol optical depth (AOD), one of the key factors affecting the atmosphere visibility, has great influence on the prediction of radiation intensity and photovoltaic power generation. Considering the problem that AOD is difficult to obtain real-timely and conveniently with high accuracy, in this paper, PM2.5 concentration, PM10 concentration and temperature, wind speed grade and relative humidity...
Functional Magnetic Resonance Imaging (fMRI) relies on multi-step data processing pipelines to accurately determine brain activity; among them, the crucial step of spatial smoothing. These pipelines are commonly suboptimal, given the local optimisation strategy they use, treating each step in isolation. With the advent of new tools for deep learning, recent work has proposed to turn these pipelines...
It is important to cut down the erection time and the operation guidance by studying the shield machine tool failure. In this paper, an ACO-BP algorithm based tool failure prediction model is established by utilizing the nonlinear mapping characteristics of neural network and mining data characteristics from the subway. According to the practical problems, the dependent variables and the independent...
Spike Timing Dependent Plasticity (STDP), wherein synaptic weights are modified based on the temporal correlation between a pair of pre- and post-synaptic (post-neuronal) spikes, is widely used to implement unsupervised learning in Spiking Neural Networks (SNNs). In general, STDP-based learning models disregard the information embedded in post-neuronal spiking frequency. We observe that updating the...
The cascade correlation neural network structure is proposed in this paper, which is used to predicting the closing price of the stocks related to state bank of India at the end of the particular day. The underlying fact of any neural network architecture is to minimize the error between the original outcome and expected result of the problem, by adjusting its weights in the architecture to the possible...
Deep convolutional networks have achieved successful performance in data mining field. However, training large networks still remains a challenge, as the training data may be insufficient and the model can easily get overfitted. Hence the training process is usually combined with a model regularization. Typical regularizers include weight decay, Dropout, etc. In this paper, we propose a novel regularizer,...
Evaluation of Latin handwriting alphabet stroke formation is a very tedious and time consuming. This task is usually dependent on experts' subjective evaluation based on handwriting legibility criteria. This paper proposes to evaluate the correctness of stroke formation from letter decomposition. Only six complex straight line alphabet explicitly L, H, A, N, K, M are used. Each letter are decomposed...
In order to select the better wine grape varieties, and improve wine quality evaluation standards, the paper made a cluster analysis of wine grape samples based on the selected 57 physicochemical indexes. It's also classified different categories of wine grape after comparing the results of the wine quality evaluation. SPSS for canonical correlation analysis was used to find typical indicators, which...
The prediction of path loss for the mobile radio signals is an important part in the design phase of the wireless cellular networks. In the process of modelling the path loss, the GSM 900 MHz signals are collected experimentally using Test Mobile System (TEMS) tool in the dense urban environment of Hyderabad city. In this paper, the best suited Cost 231 Hata empirical propagation model is implemented...
Price forecasting is one of the main issues faced in deregulated market because of the dynamic behaviour of the electricity prices. In a day-ahead pool market, market participants need forecasted prices to submit their bids to the market operator. Accurate forecast can provide a risk free environment for the producers and consumers to invest into the market. Participants themselves feel that they...
Reasonable network structure can obviously improve the learning speed and generalization ability of BP network. In this paper, an improved method to determine the number of hidden layer neurons is proposed. The method mainly takes the theory of linear correlation analysis to delete the redundant nodes and assign the weights related to others. What's more, genetic algorithm is used to optimize the...
We analyze workload traces from production data centers and focus on their VM usage patterns of CPU, memory, disk, and network bandwidth. Burstiness is a clear characteristic of many of these time series: there exist peak loads within clear periodic patterns but also within patterns that do not have clear periodicity. We present PRACTISE, a neural network based framework that can efficiently and accurately...
Because of the complex dynamic behavior of supercapacitor, its modeling must be based on parallel, distributed structures (each component has to represent a model of activity, distributed on many processing units), with learning capacity. For this purpose, the paper proposes a new feed forward artificial neural network structure with two hidden layers and with backpropagation training. The neural...
Balanced ensemble learning is developed from negative correlation learning by shifting the learning targets. Compared to the negative correlation learning, balanced ensemble learning is able to learn faster and achieve the higher accuracy on the training sets for a number of the tested classification problems. However, it has been found that the higher accuracy balanced ensemble learning obtained...
Ensemble learning system could lessen the degree of overfitting that often appear in the supervised learning process for a single learning model. However, overfitting had still been observed in negative correlation learning that is an ensemble learning method with correlation-based penalty. Two constraints were introduced into negative correlation learning in order to conquer such overfitting. One...
In positioning and navigation applications, Inertial navigation system (INS) and Global positioning system (GPS) technologies have been widely utilized. Each system has its own unique characteristics and limitations. Therefore, the integration of the two systems offers a number of advantages and overcomes each system inadequacies. The proposed schemes are implemented using the Autonomous neural networks...
Many network activities can benefit from accurate traffic classification and categorization, such as QOS control, network security monitoring, and traffic accounting. In this paper, a new approach based on feed-forward neural network is proposed for accurate traffic classification, which eliminates the disadvantages of port-based or payload-based classification methods. Extensive experimentation and...
Based on wind speed sequences, three-layer neural network model of wind speed prediction is analyzed to obtain the selecting method of neural network input, output and hidden layers' node parameters, and to predict wind speed through rolling wind speed data. In accord with the nonlinear of wind speed sequences, a BP neural network model is established to forecast wind speed. The feasibility and validity...
Since the artificial neural networks were put forward, they have been used widely in predicting, and achieved good effect. But few pay attention to what an effect input variables with the linear correlation will have on the artificial neural network. Based on one example, I analyzed and studied an influence which the input variables with linear relation have on stability and prediction effect of BP...
An artificial neural network (ANN) was applied to predict monthly shoreline changes at various locations along 25km of the Noor Bay, southern Caspian Sea. The shoreline variations in 8 stations for a period of about 11 years were studied using ANN. The model results were compared with field data. The properties of the wave (height, period, energy by different equations) and wind data were fed to a...
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