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

An effective nonparametric model for creating adaptive nonlinear filtering (ANF) algorithms is provided by kernel methods. These algorithms were developed based on different kernels like Gaussian and Laplacian. Moreover, in practical applications, nonlinear systems are also sparse in nature. So, the filter proportionate normalized least mean square (FPNLMS) algorithm was developed for such sparse nonlinear systems. In this paper to have an effective ANF algorithm, the Gaussian kernel method is used with the FPNLMS algorithm providing a novel kernel FPNLMS (KFPNLMS) algorithm for sparse environments is developed. The system identification problem is solved using the KFPNLMS method, which performs as expected when the convergence analysis is done.

Kernel Filter Proportionate Normalized Least Mean Square Algorithm for Nonlinear Sparse Systems

The multichannel Filtered-S LMS (FSLMS) algorithm has been used efficiently in nonlinear active noise control (ANC) because of its improved performance and low computational complexity. However, the performance of this algorithm has not yet been evaluated in a real-time ANC system. This correspondence shows the real-time performance of the trigonometric functional expansion based FSLMS algorithm compared to the Filtered-X LMS (FXLMS) algorithm. A multichannel active headrest with one reference, two control sources and two error microphones is used to test the algorithm. Three different primary nonlinear noise cases are studied and it is shown that in all three cases, the FSLMS algorithm is capable of attenuating the primary noise whereas the FXLMS algorithm completely fails to achieve any noise reduction. Insight into the FSLMS algorithm as a suitable noise controller for transformer noise is also presented.

Multichannel FSLMS algorithm based active headrest

The hyperbolic group of algorithms for adaptive filters has been extensively exploited in recent years to tackle the non-Gaussian noise in a system. A recently developed exponential hyperbolic sine adaptive filter (EHSAF) algorithm provided better performance compared to other algorithms developed for non-Gaussian noise. In this paper, a novel proportionate exponential hyperbolic sine adaptive filter (PEHSAF) algorithm is proposed to provide enhanced behaviour in the presence of impulsive noise for a system having sparsity in the impulse response. The number of active components compared to inactive components defines the sparsity of a system. The enhanced behaviour of the proposed PEHSAF algorithm is demonstrated by the experimental results for a sparse system identification scenario where the physical system is affected by impulsive noise and alpha-stable noise.

Proportionate Exponential Hyperbolic Sine Adaptive Filter for Sparse System Identification

Active noise control is a means of controlling the acoustic noise in real-time by generating antinoise. Therefore, computational complexity of an ANC algorithm plays a vital role in its implementation. The feedback ANC computes an extra secondary path estimate compared to the feedforward ANC and hence has more computational burden compared to the feedforward ANC. In this paper, a new frequency domain ANC algorithm is proposed that is computationally efficient compared to the standard time domain feedback ANC algorithm. The algorithm is made delayless and is suitable for real-time implementation.

Block frequency domain implementation of feedback active noise control algorithm

Seismic microzonation is defined as the process of subdividing a potential seismic or earthquake-prone area into zones with respect to some geological and geophysical characteristics of the sites such as ground shaking, liquefaction susceptibility, landslide, and rock fall hazard, earthquake-related flooding, etc. Very often, the seismic data are used from existing ground motion data that are related to other geographical regions and thus leads to unrealistic predictions. In this analysis, normalization of the available earthquake data is carried out for a better realistic prediction of building response. An extensive study is carried out in this work that involves two major types of buildings, microzones, and soil conditions. Fixed base, hard, medium, and soft soil have been considered for this analysis. The effect of actual and normalized ground motion for specific microzones having plan asymmetric and symmetric structures is not yet studied in prior research. The analysis has been done by finite element-based software. The present study makes an effort to determine the fundamental responses of plan asymmetric building in different kinds of soil in certain microzones. Maximum shear forces and bending moment have been seen in hard soil base conditions among all other supports. Responses of microzone II and actual ground motion are almost the same in dynamic analysis. 

/pdf/estimate-of-responses-of-multistoried-building-under-3qmha4o3.pdf

Debi Prasad Das

Papers

Kernel Filter Proportionate Normalized Least Mean Square Algorithm for Nonlinear Sparse Systems

Multichannel FSLMS algorithm based active headrest

Proportionate Exponential Hyperbolic Sine Adaptive Filter for Sparse System Identification

Block frequency domain implementation of feedback active noise control algorithm

Estimate of Responses of Multistoried Building under Earthquake Ground Motion to Prevent Failure