Received Jul 22, 2012 Revised Oct 23, 2012 Accepted Nov 14, 2012 Time series forecasting is an active research area that has drawn considerable attention for applications in a variety of areas. Auto-Regressive Integrated Moving Average (ARIMA) models are one of the most important time series models used in financial market forecasting over the past three decades but not very often used to forec...

The number of users and their network utilization will enumerate the traffic of the network. The accurate and timely estimation of network traffic is increasingly becoming important in achieving guaranteed Quality of Service (QoS) in a wireless network. The better QoS can be maintained in the network by admission control, inter or intra network handovers by knowing the network traffic in advanc...

In a wireless network environment accurate and timely estimation or prediction of network traffic has gained much importance in the recent past. The network applications use traffic prediction results to maintain its performance by adopting its behaviors. Network Service provider will use the prediction values in ensuring the better Quality of Service(QoS) to the network users by admission cont...

Abstract The Krishnagiri reservoir is the main source of irrigation in Tamil Nadu, India. It has been reported to be hypereutrophic for over a decade with sediment and nutrient load sources responsible degradation water quality. Remotely sensed satellite imagery analysis plays significant role assessing quality developing management strategy reservoirs. present study an attempt demonstrate impr...

Despite several individual forecasting models that have been proposed in the literature, accurate forecasting is yet one of the major challenging problems facing decision makers in various fields, especially financial markets. This is the main reason that numerous researchers have been devoted to develop strategies to improve forecasting accuracy. One of the most well established and widely use...

We study the autocorrelation structure of aggregates from a continuous-time process. The underlying continuous-time process or some of its higher derivative is assumed to be a stationary continuous-time auto-regressive fractionally integrated moving-average (CARFIMA) process with Hurst parameter H. We derive closed-form expressions for the limiting autocorrelation function and the normalized sp...

This paper presents analysis of a modified Feed Forward Multilayer Perceptron (FMP) by inserting an ARMA (Auto Regressive Moving Average) model at each neuron (processor node) with the Backp ropagation learning algorithm. The stability analysis is presented to establish the convergence theory of the Back propagation algorithm based on the Lyapunov function. Furthermore, the analysis extends the...

In this paper, a simple neural network (NN) control scheme is developed for a class of discrete-time multi-input multi-output (MIMO) non-affine nonlinear systems with triangular form inputs and disturbances. The system studied is described by NARMAX (Nonlinear Auto Regressive Moving Average with eXogenous inputs) model. Firstly, by using implicit function theorem, the existence of the implicit ...

In this paper, output feedback adaptive control is investigated for a class of nonlinear systems in output-feedback form with unknown control gains. To construct output feedback control, the system is transformed into the form of the NARMA (nonlinear-auto-regressive-moving-average) model, based on which future output prediction is carried out. With employment of the predicted future output, a c...