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.

/pdf/recent-advances-on-active-noise-control-open-issues-and-1wg2ydx57i.pdf

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

Fast communication: Fast exact multichannel FSLMS algorithm for active noise control

https://digital.library.adelaide.edu.au/dspace/bitstream/2440/78346/1/hdl_78346.pdf

A computationally efficient frequency-domain filtered-X LMS algorithm for virtual microphone

Feedback active noise control has been used for tonal noise only and it is impractical for broadband noise. In this paper, it has been proposed that the feedback ANC algorithm can be applied to a broadband noise if the noise characteristic is chaotic in nature. Chaotic noise is neither tonal nor random; it is broadband and nonlinearly predictable. It is generated from dynamic sources such as fans, airfoils, etc. Therefore, a nonlinear controller using a functional link artificial neural network is proposed in a feedback configuration to control chaotic noise. A series of synthetic chaotic noise is generated for performance evaluation of the algorithm. It is shown that the proposed nonlinear controller is capable to control the broadband chaotic noise using feedback ANC which uses only one microphone whereas the conventional filtered-X least mean square (FXLMS) algorithm is incapable for controlling this type of noise.

Nonlinear feedback active noise control for broadband chaotic noise

Adjoint nonlinear active noise control algorithm for virtual microphone

Saturation nonlinearity effect on the ANC performance is addressed.PSO based nonlinear structure ANC algorithm is proposed.Filter bank based FLANN structure is tuned with PSO.The proposed ANC algorithm does not require secondary path estimate.Computer simulations are carried out for performance evaluation.Sensitivity analysis is done for PSO parameter tuning. In this paper, a method is proposed to overcome the saturation non-linearity linked to the microphones and loudspeakers of active noise control (ANC) system. The reference microphone gets saturated when the acoustic noise at the source increases beyond the dynamic limits of the microphone. When the controller tries to drive the loudspeaker system beyond its dynamic limits, the saturation nonlinearity is also introduced into the system. The secondary path which is generally estimated with a low level auxiliary noise by a linear transfer function does not model such saturation nonlinearity. Therefore, the filtered-x least mean square (FXLMS) algorithm fails to perform when the noise level is increased. For alleviating the saturation nonlinearity effect a nonlinear functional expansion based ANC algorithm is proposed where the particle swarm optimization (PSO) algorithm is suitably applied to tune the parameters of a filter bank based functional link artificial neural network (FLANN) structure, named as PSO based nonlinear structure (PSO-NLS) algorithm. The proposed algorithm does not require any computation of secondary path estimate filtering unlike other conventional gradient based algorithms and hence has got computational advantage. The computer simulation experiments show its superior performance compared to the FXLMS, filtered-s LMS and genetic algorithms under saturation present at both at secondary and reference paths. The paper also includes a sensitivity analysis to study the effect of different parameters on ANC performance.

Debi Prasad Das

Papers

Fast communication: Fast exact multichannel FSLMS algorithm for active noise control

A computationally efficient frequency-domain filtered-X LMS algorithm for virtual microphone

Nonlinear feedback active noise control for broadband chaotic noise

Adjoint nonlinear active noise control algorithm for virtual microphone

Particle swarm optimization based nonlinear active noise control under saturation nonlinearity