An Implicit Partial Pivoting Gauss Elimination Algorithm for Linear System of Equations with Fuzzy Parameters

This paper considers the solution of fully fuzzy linear system (FFLS) by first reducing the system to crisp linear system. The novelty of this article lies in the application of Gauss elimination procedure with implicit partial pivoting to FFLS. The method is presented in detail and we use the Matlab software for implementing the algorithm. Numerical examples are illustrated to demonstrate the efficiency of the variant of Gauss elimination method for solving FFLS. Keywords: fully fuzzy linear system, fuzzy number, gauss elimination, partial pivoting, implici

Symmetric Positive Definite Based Preconditioners For Discrete Convection-diffusion Problems

Abstract — We experimentally examine the performance of preconditioners based on entries of the symmetric positive definite part and small subspace solvers for linear system of equations obtained from the high-order compact discretization of convection-diffusion equations. Numerical results are described to illustrate that the preconditioned GMRES algorithm converges in a reasonable number of iterations

Exponential Time Differencing With Runge-Kutta Time Stepping for Convectively Dominated Financial Problems

No Abstrac

Improving Artificial Neural Network Forecasts with Kalman Filtering

In this paper, we examine the use of the artificial neural network method as a forecasting technique in financial time series and the application of a Kalman filter algorithm to improve the accuracy of the model. Forecasting accuracy criteria are used to compare the two models over different set of data from different companies over a period of 750 trading days. In all the cases we find that the Kalman filter algorithm significantly adds value to the forecasting process.Keywords: Artificial Neural Networks, Kalman filter, Stock prices, Forecasting, Back propagatio

Forecasting exchange rates with linear and nonlinear models

In this paper, the exchange rate forecasting performance of neural network models are evaluated against the random walk, autoregressive moving average and generalised autoregressive conditional heteroskedasticity models. There are no guidelines available that can be used to choose the parameters of neural network models and therefore, the parameters are chosen according to what the researcher considers to be the best. Such an approach, however, implies that the risk of making bad decisions is extremely high, which could explain why in many studies, neural network models do not consistently perform better than their time series counterparts. In this paper, through extensive experimentation, the level of subjectivity in building neural network models is considerably reduced and therefore giving them a better chance of performing well. The results show that in general, neural network models perform better than the traditionally used time series models in forecasting exchange rates.exchange rates; forecasting; linear models; nonlinear models; autoregressive integrated moving average; ARIMA models; neural networks; ANNs; generalised autoregressive conditional heteroskedasticity; GARCH models; random walk models.