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

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.

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

This paper proposes a simple and novel method of designing and developing high-linearity linear variable differential transformer (LVDT)-based displacement sensing systems. Conventionally, precise adjustment of windings is made to enhance the linearity range of LVDTs. The tedious job of pitch adjustment of windings of LVDTs can be overcome by using the proposed method. A functional link artificial neural network has been successfully used in this paper for nonlinear compensation of the LVDT. The effectiveness of the proposed method is demonstrated through computer simulation with the experimental data of two different LVDT. The complete algorithm with the practical setup for the development of a linear LVDT is presented in this paper.

A Novel Method of Extending the Linearity Range of Linear Variable Differential Transformer Using Artificial Neural Network

Interpretation of mill vibration signal via wireless sensing

Many practical noises emanating from rotating machines with blades generate a mixture of tonal and the chaotic noise. The tonal component is related to the rotational speed of the machine and the chaotic component is related to the interaction of the blades with air. An active noise controller (ANC) with either linear algorithm like filtered-X least mean square (FXLMS) or nonlinear control algorithm like functional link artificial neural network (FLANN) or Volterra filtered-X LMS (VFXLMS) algorithm shows sub-optimal performance when the complete noise is used as reference signal to a single controller. However, if the tonal and the chaotic noise components are separated and separately sent to individual controller with tonal to a linear controller and chaotic to a nonlinear controller, the noise canceling performance is improved. This type of controller is termed as hybrid controller. In this paper, the separation of tonal and the chaotic signal is done by an adaptive waveform synthesis method and the antinoise of tonal component is produced by another waveform synthesizer. The adaptively separated chaotic signal is fed to a nonlinear controller using FLANN or Volterra filter to generate the antinoise of the chaotic part of the noise. Since chaotic noise is a nonlinear deterministic noise, the proposed hybrid algorithm with FLANN based controller shows better performance compared to the recently proposed linear hybrid controller. A number of computer simulation results with single and multitone frequencies and different types of chaotic noise such as logistic and Henon map are presented in the paper. The proposed FLANN based hybrid algorithm was shown to be performing the best among many previously proposed algorithms for all these noise cases including recorded noise signal.

Debi Prasad Das

Papers

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

A Novel Method of Extending the Linearity Range of Linear Variable Differential Transformer Using Artificial Neural Network

Interpretation of mill vibration signal via wireless sensing

Functional link artificial neural network applied to active noise control of a mixture of tonal and chaotic noise

Nonlinear active noise control for headrest using virtual microphone control