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To improve the reservoir long-term runoff forecasting accuracy. Adaptive regulation ant colony system algorithm (ARACS) is proposed. The forecast model is set up by using an adaptive regulation ant colony system algorithm and the radial basis function (RBF) neural network combined to form ARACS-RBF hybrid algorithm. Form the reservoir long-term runoff forecast model based on the hybrid algorithm....
Firstly, an air passenger capacity investigation at the capital international airport is made, and a composite forecasting model based on total air passenger capacity is established, in which multiple regression and ARIMA model are parallel connection and their forecast results are series connection with BP neural network. Secondly, according to the average growth rate of air passenger capacity, all...
Research on time series forecasting has been an area of considerable interest in recent decades. Several techniques have been researched for time series forecasting. There is a fundamental task in any area of knowledge of time series: use past values to predict future values from the available historical series. Thus, a very important step is to define which of these past values will be considered...
Since the current fashion color forecasts have some disadvantages in practical application, there is considerable interest in building models that can predict fashion value of the colors precisely and swiftly from historical data. This paper proposed a new forecasting model called G-LMBPNN (Gray Levenberg-Marquardt Back Propagation Neural Network). It utilizes gray process to obscure the data sequence...
This paper presents a new kind of nonlinear combination method to predict the exchange rate. The method possesses several characters as follows: (1) the short, medium and long-term influences are considered comprehensively; (2) the different singular methods are nonlinearly combined according to their characteristics; (3) Some original data is taken as the inputs of network with the forecasted values;...
At present, research on nonlinear network flows of mobile short message is one hotspot in mobile communications fields. Nonlinear network flows of mobile short message have such essential features varying with time as periodicity, regularity, correlation, randomicity, occasionality. The traditional methods based on linear models are successful relatively in making irregular flow series become more...
Annual runoff forecasting is very important for improvement of the management performance of water resources: high accuracy in runoff prediction can lead to more effective use of water resources. The purpose of this study is to apply the adaptive network based fuzzy inference system (ANFIS) model to forecast annual runoff of Yamadu hydrological station in Xinjiang Province, China. The subtractive...
Time series forecasting is useful in many researches areas. The use of models that provide a reliable prediction in financial time series may bring valuable profits for the investors. This paper proposes a methodology based on information obtained from exogenous series used in combination with neural networks to predict stock series. The best trained neural networks were used in combination to improve...
This paper uses the K-NN based nonparametric regression to forecast the short term traffic flow, applies the prediction interval calculated by K to forecast during unconventional road condition, and improves the forecasting results. Finally, nonparametric regression's advantages of high accuracy and strong transplant ability are showed while being compared with neural network.
The accurate and reliable Trip-generation Forecasting Model is the most basic and important part of the traffic forecasting model. This paper focuses on combining the neural network which has a strong fitting capability and genetic algorithm which has an excellent Global search capability with trip-generation forecasting model in order to achieve the purpose of improving the accuracy of prediction...
Logistics forecasting is useful to optimize the allocation of resources. Radial basis function(RBF) neural network has strong ability in nonlinear forecasting and high training speed. However, in the radial basis function neural network, the three parameters: the output weights, the centers of radial basis function hidden units and widths of radial basis function hidden units need to be optimized...
The aim of this paper is to propose the cooperative feature selection (CFS) to automatically select the critical factors that affect the performance of the forecasting performance of a small time series data. CFS sequentially combines grey relational analysis (GRA) and artificial neural network (ANN), which represents wrapper and filter method respectively. To test the efficiency of the proposed feature...
Salmonellosis is one of the most common seasonal zoonosis. As from the definition, zoonosis refers to the transmission of infectious diseases from animal to human. This paper presents the prediction of Salmonellosis incidence using Artificial Neural Network (ANN) on the basis of monthly data. A series of Salmonellosis incidence in US from 1993 to 2006, published by Centers for Disease Control and...
Wavelet neural network is a neural network combining the wavelet theory with neural network theory, which avoids nonlinear optimization problems such as blindness of the design of BP neural network structure and local optimum, greatly simplifying the training. The use of wavelet neural networks to forecast regional logistics demand provided an important reference for regional logistics systematic...
Exchange rate forecasting involves many challenges in research. Due to the difficulty of selecting superior variables to design a good forecasting mode, few empirical studies have discussed the influence of explainable variables. In this paper, a new forecasting model is constructed; we adopt the particle swarm optimization (PSO) to select the optimal input layer neurons to predict NTD/USD exchange...
The paper proposes a new hybrid forecasting model using auto regressive moving average (ARMA) as basic architecture and particle swarm optimization (PSO) as learning algorithm. These two combinations have yielded an efficient prediction model for retail sales volumes. To facilitate comparison ARMA, functional link artificial neural network (FLANN) and MLP models are also simulated. The performance...
Exchange rates float with non-linear dependence and long-term memory characters, so we choose the neural network tool to forecast it. Based on the previous research work, we use two groups of data at different time-intervals to make forecasting experiments with homogenous neural network model and heterogeneous neural network model. According to the results of the experiments, we make a comparative...
In the predicting financial distress, we know that irrelevant or correlated features in the samples could spoil the performance of the SVR classifier, leading to decrease of prediction accuracy. In order to solve the problems mentioned above, this paper use rough sets as a preprocessor of SVR to select a subset of input variables and employ the particle swarm optimization algorithm (PSOA) to optimize...
On the basis of studying the mechanism of water bloom, one kind of gray-BP artificial neural network forecasting method is proposed in the paper. The gray theory was used to obtain preliminary forecast of the occurrence trend of water bloom, combined with neural network to implement error compensation for the forecast result. Compared with BP, this method can predict chlorophyll change trend more...
Automobile sells system plays an important role in automobile sales area, through the whole produce and management. Some forecast models have had unilateralism in some side nowadays, such as ARMA model. For example, the data of non-linearity has some error by ARMA model. This paper, assembles curve -regression model, Time Series Decomposition Model and RBF neural networks according to the weight distribution...
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