In this paper a Bayesian regularized artificial neural network is proposed as a novel method to forecast financial market behavior Daily market prices and financial technical indicators are utilized as inputs to predict the one day future closing price of individual stocks The prediction of stock price movement is generally considered to be a challenging and important task for financial time series analysis The accurate prediction of stock price movements could play an important role in helping investors improve stock returns The complexity in predicting these trends lies in the inherent noise and volatility in daily stock price movement The Bayesian regularized network assigns a probabilistic nature to the network weights, allowing the network to automatically and optimally penalize excessively complex models The proposed technique reduces the potential for overfitting and overtraining, improving the prediction quality and generalization of the network Experiments were performed with Microsoft Corp and Goldman Sachs Group Inc stock to determine the effectiveness of the model The results indicate that the proposed model performs as well as the more advanced models without the need for preprocessing of data, seasonality testing, or cycle analysis

A Bayesian regularized artificial neural network for stock market forecasting

The paper proposed an improved bacterial chemotaxis optimization (IBCO), which is then integrated into the back propagation (BP) artificial neural network to develop an efficient forecasting model for prediction of various stock indices. Experiments show its better performance than other methods in learning ability and generalization.

Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network

Extensive calculations based on density functional theory have been carried out to understand the origin of magnetism in undoped ZnO thin films as found in recent experiments. The observed magnetism is confirmed to be due to Zn, instead of O, vacancy. The main source of the magnetic moment, however, arises from the unpaired 2p electrons at O sites surrounding the Zn vacancy with each nearest-neighbor O atom carrying a magnetic moment ranging from 0.490 to 0.740 B. Moreover, the study of vacancy-vacancy interactions indicates that in the ground state, the magnetic moments induced by Zn vacancies prefer to ferromagnetically couple with the antiferromagnetic state lying 44 meV higher in energy. Since this is larger than the thermal energy at room temperature, the ferromagnetic state can be stable against thermal fluctuations. Calculations and discussions are also extended to ZnO nanowires that have larger surface to volume ratio. Here, the Zn vacancies are found to lead to the ferromagnetic state too. The present theoretical study not only demonstrates that ZnO samples can be magnetic even without transition-metal doping but also suggests that introducing Zn vacancy is a natural and an effective way to fabricate magnetic ZnO nanostructures. In addition, vacancy mediated magnetic ZnO nanostructures may have certain advantages over transition-metal doped systems in biomedical applications.

https://absimage.aps.org/image/MAR08/MWS_MAR08-2007-004197.pdf

Zinc Vacancy induced magnetism in ZnO thin films and nanowires

The problem of acoustic noise is becoming increasingly serious with the growing use of industrial and medical equipment, appliances, and consumer electronics. Active noise control (ANC), based on the principle of superposition, was developed in the early 20th century to help reduce noise. However, ANC is still not widely used owing to the effectiveness of control algorithms, and to the physical and economical constraints of practical applications. In this paper, we briefly introduce some fundamental ANC algorithms and theoretical analyses, and focus on recent advances on signal processing algorithms, implementation techniques, challenges for innovative applications, and open issues for further research and development of ANC systems.

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Recent Advances on Active Noise Control: Open Issues and Innovative Applications

Vehicle system dynamics integration encompasses interdisciplinary challenges innovations in various aspects related to vehicle system/subsystems/components dynamic characteristics, modeling and validation, vehicle dynamics state measurement and estimation, vehicle/chassis control systems, coordination of power management and dynamics/stability control, etc [1-4]. These are becoming more critical due to the increasing concern and rapid development in electric and hybrid vehicles, which tend to exhibit different vehicle dynamics/stability and control characteristics when compared to conventional vehicles.

Mechanical systems and signal processing

In many practical applications the acoustic noise generated from dynamical systems is nonlinear and deterministic or stochastic, colored, and non-Gaussian. It has been reported that the linear techniques used to control such noise exhibit degradation in performance. In addition, the actuators of an active noise control (ANC) system very often have nonminimum-phase response. A linear controller under such situations can not model the inverse of the actuator, and hence yields poor performance. A novel filtered-s least mean square (FSLMS) algorithm based ANC structure, which functions as a nonlinear controller, is proposed in this paper. A fast implementation scheme of the FSLMS algorithm is also presented. Computer simulations have been carried out to demonstrate that the proposed algorithm outperforms the standard filtered-x least mean square (FXLMS) algorithm and even performs better than the recently proposed Volterra filtered-x least mean square (VFXLMS) algorithm, in terms of mean square error (MSE), for active control of nonlinear noise processes. An evaluation of the computational requirements shows that the FSLMS algorithm offers a computational advantage over VFXLMS when the secondary path estimate is of length less than 6. However, the fast implementation of the FSLMS algorithm substantially reduces the number of operations compared to that of FSLMS as well as VFXLMS algorithm.

Active mitigation of nonlinear noise Processes using a novel filtered-s LMS algorithm

This correspondence proposes a novel nonlinear adaptive algorithm named as filtered-s least mean square (FSLMS) algorithm for multichannel active control of nonlinear noise processes. A reduced complexity FSLMS algorithm using filter bank approach is also suggested. The performance of the proposed algorithm is validated through computer simulations for nonlinear noise processes. It is demonstrated that the proposed method outperforms the conventional filtered-x least mean square algorithm and second-order Volterra filtered-x LMS (VFXLMS) algorithm for control of nonlinear noise processes. Computational complexity analysis shows the proposed method involves lesser number of computations as compared to second-order VFXLMS algorithm

Filtered-s LMS algorithm for multichannel active control of nonlinear noise processes

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.

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

In this paper, particle swarm optimization (PSO) algorithm, which is a nongradient but simple evolutionary computing-type algorithm, is proposed for developing an efficient active noise control (ANC) system. The ANC is conventionally used to control low-frequency acoustic noise by employing a gradient-optimization-based filtered-X least mean square (FXLMS) algorithm. Hence, there is a possibility that the performance of the ANC may be trapped by local minima problem. In addition, the conventional FXLMS algorithm needs prior identification of the secondary path. The proposed PSO-based ANC algorithm does not require the estimation of secondary path transfer function unlike FXLMS algorithm and, hence, is immune to time-varying nature of the secondary path. In this investigation, a small modification is incorporated in the conventional PSO algorithm to develop a conditional reinitialized PSO algorithm to suit to the time-varying plants of the ANC system. Systematic computer simulation studies are carried out to evaluate the performance of the new PSO-based ANC algorithm.

Particle Swarm Optimization Based Active Noise Control Algorithm Without Secondary Path Identification

This correspondence attempts to derive the exact implementation of two nonlinear active noise control (ANC) algorithms, viz. FSLMS and VFXLMS. The concept of reutilizing a part of the computations performed for the first sample while computing the next sample, for a block length of two samples, is exploited here to implement the fast and exact versions of the FSLMS and VFXLMS algorithms which are computationally efficient. Detailed computational complexity analysis for both addition and multiplication requirements is presented to show the advantage of the proposed algorithms. Appropriate simulation experiments are carried out to compare the performance equivalence of the proposed fast algorithms with their original versions.

Debi Prasad Das

Papers

Active mitigation of nonlinear noise Processes using a novel filtered-s LMS algorithm

Filtered-s LMS algorithm for multichannel active control of nonlinear noise processes

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

Particle Swarm Optimization Based Active Noise Control Algorithm Without Secondary Path Identification

Fast Adaptive Algorithms for Active Control of Nonlinear Noise Processes