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Expanding mathematical models and forecasting the traffic flow is a crucial case in studying the dynamic behaviors of the traffic systems these days. Artificial Neural Networks (ANNs) are of the technologies presented recently that can be used in the intelligent transportation system field. In this paper, two different algorithms, the Multi-Layer Perceptron (MLP) and the Radial Basis Function (RBF)...
Energy consumption predictions are essential and are required in the studies of capacity expansion, energy supply strategy, capital investment, revenue analysis and market research management. In the recent years artificial neural networks (ANN) have attracted much attention and many interesting ANN applications have been reported in power system areas, due to their computational speed, their ability...
Micro alloyed steels mechanical properties can be modified by nano-modification based on aging heat treatments inducing different levels of nano precipitates on their surface microstructure. Under sour corrosion, electrochemical impedance spectroscopy (EIS) technique could serve to identify the modified micro alloyed steel corrosion properties. This paper present a predictive model for EIS-Ny quist...
In this paper we propose a methodology for improving the accuracy of models that predict self-reported player pairwise preferences. Our approach extends neuro-evolutionary preference learning by embedding a player modeling module for the prediction of player preferences. Player types are identified using self-organization and feed the preference learner. Our experiments on a dataset derived from a...
In this paper, we propose a novel tree based modeling method, Generalized Cluster based Fuzzy Model Tree (G-CFMT) which can model piecewise linear or piecewise nonlinear dataset and predict a continuous output value. To construct the G-CFMT, data cluster centers are calculated by fuzzy clustering and Extreme Learning Machine (ELM) are obtained at the tree nodes. Since the fuzzy clustering method can...
For urban water consumption characteristics of the process will be applied to the neural network model for forecasting urban water demand. Describes the process of feed-forward neural network model and the feedback process neural network model; study of weight function based on orthogonal basis started learning algorithm, and describes the specific steps of the algorithm; drawn flowchart of urban...
This paper applies DEA model to a sample of 58 power plate listed companies in the securities market in China in 2008, with a view to identifying the financial risk companies and non-financial risk companies, instead of using ST in the past. Then, after comparing logit regression model and neural network LVQ in predicting the company financial risks, the conclusion was drawn that neural network LVQ...
An Artificial neural network analysis model for earthquake-damaged, which couples geographic information systems(GIS) with artificial neural networks (ANN) to predict the seismic damage to multistory buildings based on earthquake intensity and adopt the peak acceleration value, is presented here. ANN is used to learn the patterns of development in the region and test the predictive capacity of the...
Based on the neural network theory, this paper proposes the neural network model to solve the surrounding rock displacement prediction of nonlinear problems. This model combines the advantages of wavelet time-frequency analysis and neural network self-learning. The studies had shown that the wavelet neural network had higher prediction accuracy. In addition, it could better reveal the changes of displacement...
A reliable and accurate short-term traffic forecasting system is crucial for the successful deployment of any intelligent transportation system. To address the complexity of real-world traffic forecasting conditions, this paper presents a layered traffic forecasting algorithm, which is implemented by a clustering neural network, Kohonen self-organizing map (KSOM) and four neural network paradigms...
One of the important requirements for operational planning of electrical utilities is the prediction of hourly load up to several days, known as short term load forecasting (STLF). Considering the effect of its accuracy on system security and also economical aspects, there is an on-going attention toward putting new approaches to the task. Recently, neuro fuzzy modeling has played a successful role...
An artificial neural network model forecasting diameter distribution of stands was created by using artificial neural network modeling technology, in Masson pine planted forest. Through training and optimum seeking, the idea model was created, in which the model structure is 3:6:6:1, the training error is 0.000281, and the total fitting accuracy is 98%. Concretely, the mean frequency fitting accuracy...
Because the damage of pipeline is controlled by many factors, such as fault movement, pipe-soil interaction, buried depth, etc., the relationship between pipeline damage and influencing factors is complicated. In order to predict the pipeline damage, predictive model is constructed on the basis of artificial neural network (ANN), in which the damage of pipeline becomes a nonlinear function of influence...
The neural network model presents a new procedure and structure in nonlinear interpolation and demonstrates a potential of stock market prediction with incomplete, imprecise and noisy data. However, a random selection of model parameters might cause the ??over-fitting?? or ??under-fitting?? problem in generalization or prediction. This paper presents a sensitivity analysis of optimal hidden layers...
The traditional prediction model is not able to achieve a satisfying prediction effect in the problem of a non-linear system and nonstationary financial signal. The existing wavelet neural network has overcome the deficiency of traditional prediction model which is limited to linear system when predicting. However, wavelet neural network has a defect of confusing signal frequency. Based on the theory...
To accurately obtain dynamic characteristics of a heat exchanger, black-box modeling method and gray-box modeling method were used with the help of neural network technology. The black-box model directly used the heat exchanger's input and output data to train the neural network. It constantly adjusted the network's weight to record the system's dynamic characteristics, and then predict output. Having...
A multiresolution-based echo state network (MESN) based on echo state network (ESN) is proposed in this paper. ESN proves to be very efficient for modeling and time series prediction. The learning process of MESN was further improved by using a multiresolution-based learning algorithm. The proposed MESN was applied to the long-term prediction of real network traffic and its performance was compared...
Attempts to recognize pattern of monsoon rainfall over the smaller scale geographical region (district) 8-Parameter Probabilistic ANN model have been developed. Eleven neurons in input layer to input eleven years rainfall data time series. Eleven neurons in hidden layer, one output neuron for observation of twelve year rainfall, 132 trainable weights in three layers, transfer function sigmoid f (x)...
This paper proposes a combination of methodologies based on a recent development -called Extreme Learning Machine (ELM)- decreasing drastically the training time of nonlinear models. Variable selection is beforehand performed on the original dataset, using the Partial Least Squares (PLS) and a projection based on Nonparametric Noise Estimation (NNE), to ensure proper results by the ELM method. Then,...
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