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Since the introduction of artificial neural networks (ANN) the numerous investigations of concrete systems had been proposed. But further development of the theory and applications of networks follows to the investigations of new examples of dependent of time systems with anticipatory property. New special class of anticipatory systems had been introduced by D, Dubois — namely the system with strong...
Prognostics and prediction of patients' short term physiological health status are of critical importance in medicine because they afford medical interventions that prevent escalating medical complications. This study proposes a prognostics engine to predict patient physiological status. The prognostics engine builds models from historical clinical data using neural network as its computational kernel...
Attracting more students into science and engineering disciplines concerned many researchers for decades. Literature used traditional statistical methods and qualitative techniques to identify factors that affect student retention up most and predict their persistence. In this paper we developed two neural network models using a feed-forward backpropagation network to predict retention for students...
The aim of the work is verifying the possibility of extrapolating information on demand trends, for a company specialized in the production of aluminium tins, using the data collected in previous periods. This study is mainly divided into three stages: (1) data pre-processing (data collection) stage, (2) adaptive network evaluating stage and (3) forecast and recall stage. At the stage of data collection,...
The prediction for dissolved oxygen (DO) in aquaculture ponds is a problem of multi-variables, nonlinearity and long-time lag. Neural networks (NNs) have become one of ideal tools in modeling nonlinear relationship between inputs and outputs. In this work, GA-LM, a neural network model combining Levenberg-Marquardt(LM) algorithm and Genetic Algorithm (GA) was developed for predicting DO in an aquaculture...
In this paper, we studied the two most commonly used artificial intelligence methods (Multilayer Perceptron and Radial Basis Function network) to build the credit scoring model of applications, and analyzed the most important restraining factors of the applications of neural network which is the exponential increase in the variables bringing the model over-complex. On this basis, the author combines...
Data mining (DM) is the extraction of hidden predictive information from large databases that has becoming a powerful new technology with great potential to help companies to focus on the most important information in their data warehouses. A predictive model makes a prediction about values of data using known results found from historical data where the best possible outcome based on the previous...
A new model is introduced in this paper to construct the input-output relation in the prediction and control problem of non-analytic systems. The historical input-output data of general system is de-noised with wavelet transformation and SVM, and the input-output variables which can reflect the features of the system are determined with correlation analysis and sensitivity analysis. With the historical...
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...
Based on the measured data of hillslope simulated rainfall experiment in the Loess Plateau of China, the method of back-propagation neural networks optimized by genetic algorithms was used to establish the hillslope runoff and infiltration model. The rainfall intensity, rainfall duration, initial soil water content and slope were selected as the model inputs, the runoff volume and infiltration volume...
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...
This study using neural network method for estimating VAR in emerging stock markets include Chinese and Hong Kong stock markets. Empirical results showed that the neural network method has outperformed conventional methods (historical simulation (HS), variance/covariance and the Monte Carlo simulation) in estimating VAR.
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...
Accurate short term load forecasting (STLF) is a prerequisite for proper generation scheduling and reliable operation of power utilities. Conventional methods of STLF, suffer from the disadvantages such as lack of ability to accurately model the weather parameters affecting the load, lack of robustness for representing weekends and public holidays and of being computation intensive. Application of...
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...
Negotiations are one of the most common ways that agents in a multi-agent system use to reach agreements. As negotiations commonly are multi-lateral and multi-issue, these processes become more difficult. Moreover, in real-world applications in which real-time agents are needed, this issue becomes more important. Autonomous agents should be able to decide what to propose in each round of negotiations...
In this paper an ARIMA model is used for time-series forecast involving wind speed measurements. Results are compared with the performance of a back propagation type NNT. Results show that ARIMA model is better than NNT for short time-intervals to forecast (10 minutes, 1 hour, 2 hours and 4 hours). Data was acquired from a unit located in Southern Andalusia (Pentildeaflor, Sevilla), with a soft orography...
Since the implementation of the new mechanism of renminbi exchange rates in 2005, their fluctuation range has become more greater. Therefore, it is very important to control renminbi exchange rates risk via forecasting. This paper describes four alternative renminbi exchange rates forecasting models. These models are based on autocorrelation shell representation and neural networks techniques. An...
Prediction of stock prices is an issue of interest to financial markets. Many prediction techniques have been reported in stock forecasting. Neural networks are viewed as one of the more suitable techniques. In this study, an experiment on the forecasting of the stock exchange of Thailand (SET) was conducted by using feedforward backpropagation neural networks. In the experiment, many combinations...
In this study, we propose a business intelligent model integrating econometric models, i.e. ARMA, GARCH, and ANN models for VaR estimation. The business intelligent model achieves better efficiency in input variables selecting because they are selected and newly created by time series models. Repetitive trial error process could be effectively eliminated to one time series process. On the other hand,...
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