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Various governments and stakeholders are established across the globe to respond to various energy challenges that has led to one or more energy policy development. A proper analysis of what contributes to energy consumption will assist in the development of policies needed for the conservation of energy consumption. This study made use of the connection weight approach as an instrument of the Artificial...
In order to get the excellent accuracy for price forecast in the steel market, the adaptive Radial Basis Function (RBF) Neural Network (NN) model, Back Propagation (BP) NN model and Sliding Window (SW) model are utilized to forecast the price of the steel products in this paper. Eight steel products, which extracted from Shanghai Baoshan steel market of China at January, 2011 to December 2011, are...
Diameter distribution is used to predict stand stock, timber volume and stand yield in most forest management. In the paper, opulus shelterbelts in Boai County were analyzed. A model to predict stand diameter distribution was constructed with artificial neural network(ANN) approach by using the average stand diameter, the coefficient of variation of diameter as well as relative diameter as input variables,...
The current research of travel mode choice of residents focused on internal transport and urban transport mode choice, most research focused on the design of disaggregate model. As a Chinese special phenomenon of transport, the “Spring Festival Transportation” affects millions of people's quality of life. Qualitative research on travel mode choice of residents during the Spring Festival is used to...
Neural Network is a network that resembles a human brain tissue, which may infer a result based on the facts or experience that happened. Many applications have implemented neural network. In this thesis, we compared the stock forecasting result of ANTM (PT Aneka Tam bang) using Artificial Neural Network and ARIMA. ARIMA is a technique of time-series forecasting, which means forecast based on the...
A novel Neural Network Based Fuzzy Inference System for financial default forecast will be introduced. A wide range of financial forecasts is known. This method is focusing on the economical default forecast, but the method can be used generally for other financial forecasts as well, for example for calculating the Value at Risk. This hybrid method is combined by two classical methods: the artificial...
Objective Forecast and analysis of cerebral infraction incidence rate are the basis and key work of cerebral infraction prevention and control. At present, forecast of cerebral infraction incidence rate is mainly based on traditional research approach or single artificial neural network technology. Recent study results show that combined forecast model approach enjoys more precise forecast than monomial...
In this paper, we select the Elman neural network method to improve because of its good non-linear effect of disturbance elimination, and present a new exchange rate time series prediction method. We point out a new improved Elman neural network model firstly, and then predict the time series of RMB exchange rate against U. S. dollar. Through the forecasting process, we determine the input variables...
Prediction of monsoon rainfall in a timely manner can be highly beneficial for Pakistan, where monsoon is the major source of rain. Presently, Multiple Linear Regression and Statistical Downscaling Models are being used for monsoon rainfall prediction. In spite of making use of a large number of resources and having dependency on a number of parameters, the results of these models have not been satisfactory...
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...
Urbanization is the general trend and tide of the current world development and also one of the most remarkable social and economical phenomena in the world. Urbanization level becomes an important symbol of the economic strength and modernization level in a region and thus how to improve the local urbanization level has become a priority for economic development. With the increasing urbanization...
Algal bloom model in Xiangxi Bay of Three Gorges Reservoir was established by artificial neural network (ANN) technology. Using stepwise multiple linear regression method, the important environmental factors (dissolved oxygen, pH, total nitrogen, ammonium nitrogen and silicate) were selected as input variables in ANN model. The optimal structure of the ANN model was determined based on leave one out...
Waveform signals of earthquake and explosion are nonlinear and non-stationary. A BP (Back-Propagation) neural network model is established to simulate the waveform signal of the earthquake and explosion based upon the real wave data on the Matlab 7.0 experiment platform. It is shown that the differences between the waveform signal simulated by BP neural network and the real waveform signal are quite...
Through literature consulting and correlation analysis, this paper selects seven important indicators which have close relation with CPI. Then on the basis of neural network theory and MATLAB neural network toolbox, this paper constructs a CPI prediction model. Finally, by using test samples to make a emulate experiment, the simulation result indicates that the model is feasible and effectual.
This paper presents a neural network (NN) approach for determining the best design combination of product form elements that match a given product value represented by eco-product value (EpV) attributes. Twenty-seven representative office chairs are derived from 100 collected as the experimental samples by using multidimensional scaling and cluster analysis. Moreover, a morphological analysis is applied...
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...
This paper presents a neural network (NN) approach for determining the design combination of product form elements that match a given eco-product value (EPV) and product image. A morphological analysis is used to extract form elements from these sample office chairs. The experimental study identifies 7 office chair design elements and 27 representative office chairs as experimental samples for developing...
With the development of the ocean economy exploitation, study on carrying capacity and its dynamic changes are the key methods for all-sided and correct understanding of the relationship between human being and ocean, for scientific management or utilization of ocean resources and realization of ocean sustainable development. This paper presents a Data-Driven Model (DDM) to establish carrying capacity...
The last decade witnessed a significant increase in net private capital inflows in China. Some of them are short-term capital flows, which are typically considered to be highly volatile. For effectively forecasting the short-term capital flows, a three-layered neural feedforward network was employed in this paper. In light of the weakness of the conventional Back-Propagation algorithm, the Levenberg-Marquardt...
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...
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