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Application of neural networks for direct prediction of lateral-directional force and moments coefficients from the measured flight data of the research aircraft is proposed in this paper. Proposed model of neural networks appears to be a suitable practical approach to develop relationship between flight variables. This relationship eliminates the need of aerodynamic model as well as thrust model...
This paper introduces Generalized Regression Neural Network (GRNN) for long term wind speed prediction of major wind power potential states in India. The performance of proposed GRNN model is evaluated using the publicly available online dataset of National Aeronautics and Space Administration (NASA). Data samples of 26 cities are used for training the generalized regression neural network and remaining...
In the application of Tropical Cyclone Track (TCT) forecast in South China Sea (SCS), pure linear neural network (PLNN) is used as the expert in the committee machine model, and it partly determines the model output. Data normalization is one of the most important factors, which affect the performance of the individual expert net. This paper aims to find how much data normalization affects the convergence...
The growing rate of urban and industrial development leads to high levels of air pollution in most countries around the world. Because air pollution has a major impact on human health, monitoring and forecasting of the most important pollutants concentrations are very important. The modelling of the non-linear and complex phenomena associated to air pollution is successfully performed using artificial...
Software changes are inevitable during maintenance, Object-oriented software (OOS) in particular. For change not to be performed in the “dark”, software change impact analysis (SCIA) is used. However, due to the exponential growth in the size and complexity of OOS, classes are not without faults and the existing SCIA techniques only predict change impact set. This means that a change implemented on...
The Off-Grid systems are systems with an independence on the energy supply from external grid, whereas renewables (RES) are used as a sources of electric and heat energy. The main RES is photovoltaic power plant (PVP), however this source has the stochastic character of power supply. The stochastic character of PVP is given by dependency on a weather conditions. This brings a need of solar irradiance...
The main goal of the work presented in this paper was to develop a set of algorithms which allows to predict what will be the probability ratio of acquisition of the items form the given database. To fulfill this goal, the appropriate statistical methods were developed, mainly using R programming language. In order to apply the specific statistical methods, the appropriate database preprocessing was...
The deteriorating quality of natural water resources like lakes, streams and estuaries, is one of the direst and most worrisome issues faced by humanity. The effects of un-clean water are far-reaching, impacting every aspect of life. Therefore, management of water resources is very crucial in order to optimize the quality of water. The effects of water contamination can be tackled efficiently if data...
A neural network family is commonly used for improving financial forecasting accuracy. This paper proposes a feedback functional link artificial neural network (FFLANN) for the prediction of net asset value (NAV) of Indian Mutual funds which incorporates fewer computational load and fast forecasting capability. It is clear from the root mean square error (RMSE) and mean absolute percentage error (MAPE)...
The call center provides customer services to the customer of a company. Call center agents play an important role in such services. To ensure the quality of customer service, agent training and evaluation are essential. Usually, agents are monthly evaluated by their supervisor. Nevertheless, an objective evaluation standard is desired. Twenty three quantitative indicators for call center operations...
In stock market, successful investors can earn maximum profits depended on a stock selection and a suitable time on trading. Generally, investors use two statistical techniques for making a decision, which are the fundamental analysis and the technical analysis. Recently, machine learning models which are a part of artificial intelligence, has been applied to enhance investors for investment. A number...
While training a model with data from a dataset, we have to think of an ideal way to do so. The training should be done in such a way that while the model has enough instances to train on, they should not over-fit the model and at the same time, it must be considered that if there are not enough instances to train on, the model would not be trained properly and would give poor results when used for...
Software is an entity that keeps on progressing and endures continuous changes, in order to boost its functionality and maintain its effectiveness. During the development of software, even with advanced planning, well documentation and proper process control, are problems that are countered. These defects influence the quality of software in one way or the other which may result into failure. Therefore,...
Forecasting traffic flow is a popular research topic in Intelligent Transportation System. There have been several methods used for this forecasting, such as statistical methods, Bayesian Network, Neural Network Model, Hybrid ARIMA and ANN. Generalized Regression Neural Network (GRNN) is an interesting model to be used in forecasting traffic flow, as it can predict data with dynamic change and non-linear...
With the continuous shrinking of technology nodes, lithography hotspot detection and elimination in the physical verification phase is of great value. Recently machine learning and pattern matching based methods have been extensively studied to overcome runtime overhead problem of expensive full-chip lithography simulation. However, there is still much room for improvement in terms of accuracy and...
Data mining rely on large amount of data to make learning model and the quality of data is very important. One of the important problem under data quality is the presence of missing values. Missing values can occur in both at the time of training and at the time of testing. There are many methods proposed to deal with missing values in training data. Many of them resort to imputation techniques. However,...
In This paper, a new method for handling the missing data in the Partial Least Squares (PLS) regression method is proposed. The idea to handle missing data is by filling the empty field internally by the regression among predictors. First the PLS regression model is built on the data with no missing values which will have a predictor set and a response set. Then we consider the data with missing values...
Fault prediction techniques aim to predict faulty module in order to reduce the effort to be applied in later phase of software development. Majority of the approaches available in literature for fault prediction are based on regression analysis and neural network techniques. It is observed that numerous software metrics are also being used as input for fault prediction. In this paper, a cost evaluation...
In the procedure of China's market-oriented reforms of interest rates, the interest-rate risk becomes increasingly apparent. Analyzing the existing studies and the interbank offered rate trend, this paper finds that LS-SVM, which is short for Least Squares Support Vector Machines, is excel in nonlinear data approximation, which is suitable for interest rate forecasting. Firstly, the paper established...
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