The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
An upper bound for the smallest period of the Fibonacci module sequence is obtained, which is 6N, and more accurate and convenient than the current results. Since the smallest period of the Fibonacci module sequence is twice of that of two-dimensional Arnold transformation, the upper bound for the smallest module period of Arnold transformation is naturally 3N. It is a great advance compared with...
To find effective estimations of tail dependence, we present the estimators of upper tail dependence coefficient by using survival copula. We research to two problems by using the samples from t-copula. Firstly, do the estimators estimate effectively the upper tail dependence coefficients of copula? Which is the best among the estimators? Secondly, if sample isn't from true distribution, do the estimators...
Performance is an important non functional aspect to be considered for any software system. Software Performance Engineering (SPE) is an approach to predict the performance of a software system early in the life cycle. In this paper we present a neural network model for the performance prediction of Multi-Agent system at the early stages of development. We used Feed forward back propagation neural...
In this paper we propose a robust channel estimator for Long Term Evolution (LTE) downlink highly selective using neural network. This method uses the information provided by the reference signals to estimate the total frequency response of the channel in two phases. In the first phase, the proposed method learns to adapt to the channel variations, and in the second phase it predicts the channel parameters...
A Multilayer Perceptron (MLP) neural network based approach for estimation of the voltage stability L-index in a power system with Plug-in Electric Vehicles (PEVs) is presented. This technique overcomes the limitations of direct calculation of L-index from measurements at a load bus. The L-index calculation is dependent upon the no-load voltage phasor for any given system topology and operating condition...
Feed-Forward Neural Network (FFNN) has recently been utilized to estimate blood pressure (BP) from the oscillometric measurements. However, there has been no study till now that consolidated the role played by the different neural network (NN) training algorithms in affecting the BP estimates. This paper compares the estimation errors in the BP due to ten different training algorithms belonging to...
This paper proposes maximum torque control of IPMSM drive using multi model reference adaptive fuzzy controller (Multi-MFC) and artificial neural network (ANN). This control method is applicable over the entire speed range which considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal-axis current for maximum torque operation...
This paper presents computational intelligence techniques for software cost estimation. We proposed a new recurrent architecture for genetic programming (GP) in the process. Three linear ensembles based on (i) arithmetic mean (ii) geometric mean and (iii) harmonic mean are implemented. We also performed GP based feature selection. The efficacy of these techniques viz multiple linear regression, polynomial...
The paper introduces an approach for linking and decay coefficient estimation in the optimized pulse coupled neural network with modified feeding input (OM-PCNN). These two parameters have significant importance to improve the image recognition precision. The standard PCNN has ten parameters, so it is very difficult to find optimal closed system of all its parameters. By introducing the OM-PCNN it...
This paper deals with the design, analysis and simulation of an online voltage envelope estimator and an online feed-forward neural network (FFNN) controller based distributed static compensator (DSTATCOM) controller. Existing controllers such as PI controller and offline neural network controller are fixed structures and provide satisfactory control only for certain problems for which they are designed...
This paper presents a new method of on-line estimation for the stator and rotor resistances of the induction motor in the indirect vector controlled drive, using artificial neural networks. The back propagation algorithm is used for training of the neural networks. The error between the rotor flux linkages based on a neural network model and a voltage model is back propagated to adjust the weights...
Maglev train is a new vehicle without support wheel and its movement speed is gained through a special measure equipment. The paper proposes a neural network arithmetic for the maglev train speed estimator which combines characteristics of its traction linear induction motor. The result of a dynamic simulation experiment shows that real speed measure is near to theory calculation. This proves that...
Leaf being the basic component of almost all plants on the earth, its biochemical status controls many critical physiological and ecological processes including photosynthesis and primary production that are crucial to our living environment. Leaf reflectance spectrum, which is caused by the absorption of leaf biochemical substances to a great extent, becomes an effective and fast way for leaf biochemical...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.