Showing papers by "Debi Prasad Das published in 2007"

New block filtered-X LMS algorithms for active noise control systems

Abstract: New block formulations for an active noise control (ANC) system using only convolution machines are presented. The proposed approaches are different from conventional block least-mean-square (LMS) algorithms that use both convolution and cross-correlation machines. The block implementation is also applied to the filtering of the reference signal by the secondary-path estimate. In addition to the use of the fast Fourier transform (FFT), the fast Hartley transform (FHT) is used to develop transform-domain ANC structures for reducing computational complexity. In the proposed approach, some FFT and FHT blocks are removed to obtain an additional reduction of the computational burden resulting in the reduced-structure of FFT-based block filtered-X LMS (FBFXLMS) and FHT-based block filtered-X LMS (HBFXLMS) algorithms. The computational complexities of these new ANC structures are evaluated.

Stock market prediction of S&P 500 and DJIA using Bacterial Foraging Optimization Technique

Abstract: The present paper introduces the bacterial foraging optimization (BFO) technique to develop an efficient forecasting model for prediction of various stock indices. The connecting weights of the adaptive linear combiner based model are optimized by the BFO so that its mean square error(MSE) is minimized. The short and long term prediction performance of the model is evaluated with test data and the results obtained are compared with those obtained from the multilayer perceptron (MLP) based model. It is in general observed that the proposed model is computationally more efficient, prediction wise more accurate and takes less training time compared to the standard MLP based model.