scholarly journals Stock Market Prediction Using Artificial Neural Networks

2012 ◽  
Vol 6-7 ◽  
pp. 1055-1060 ◽  
Author(s):  
Yang Bing ◽  
Jian Kun Hao ◽  
Si Chang Zhang
Keyword(s):  
Neural Network ◽  
Prediction Models ◽  
Learning Algorithm ◽  
Stock Exchange ◽  
Back Propagation ◽  
Composite Index ◽  
Network Models ◽  
Back Propagation Neural Network ◽  
Neural Network Models ◽  
The Neural Network

In this study we apply back propagation Neural Network models to predict the daily Shanghai Stock Exchange Composite Index. The learning algorithm and gradient search technique are constructed in the models. We evaluate the prediction models and conclude that the Shanghai Stock Exchange Composite Index is predictable in the short term. Empirical study shows that the Neural Network models is successfully applied to predict the daily highest, lowest, and closing value of the Shanghai Stock Exchange Composite Index, but it can not predict the return rate of the Shanghai Stock Exchange Composite Index in short terms.

scholarly journals Experimental and modelling studies on antifungal compounds

2006 ◽  
Vol 4 (3) ◽  
pp. 428-439 ◽  
Author(s):  
Mukesh Doble ◽  
K. Kumar
Keyword(s):  
Neural Network ◽  
Aspergillus Niger ◽  
Organic Compounds ◽  
Back Propagation ◽  
Network Models ◽  
Back Propagation Neural Network ◽  
Neural Network Models ◽  
The Neural Network ◽  
Qsar Models ◽  
Modelling Studies

AbstractAntifungal activity of organic compounds (aromatic, salicylic derivatives, cinnamyl derivatives etc) on Fusarium Rosium (14 compounds) and Aspergillus niger (17 compounds) was studied and QSAR models were developed relating molecular descriptors with the observed activity. Back propagation Neural Network models and single and multiple regression models were tested for predicting the observed activity. The data fit as well as the predictive capability of the neural network models were satisfactory (R2 = 0.84, q2 = 0.73 for Fusarium Rosium and R2 = 0.75, q2 = 0.62 for Aspergillus niger). The descriptors used in the network for the former were X4 (connectivity) and Jhetv (topological); and TIC1 (information) and SPI (topological) for the latter fungus. Antifungal activities of these organic compounds were generally lower against the latter than with the former fungus.

Modeling of Magnetorheological Damper Using Back Propagation Neural Network

2014 ◽  
Vol 896 ◽  
pp. 396-400 ◽  
Author(s):  
Ubaidillah ◽  
Gigih Priyandoko ◽  
Muhammad Nizam ◽  
Iwan Yahya
Keyword(s):  
Neural Network ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Magnetorheological Damper ◽  
Model Parameters ◽  
Test Machine ◽  
The Neural Network ◽  
Time Histories ◽  
Magneto Rheological

This paper presents a new approach to model magneto-rheological (MR) dampers for semi-active suspension systems. The neural network method using adaptive back-propagation learning algorithm real is proposed. The experimental data collected from suspension test machine consist in time histories of current, displacement, velocity and force measured both for constant and variable current. The model parameters are determined using a set of experimental measurements corresponding to different current constant values. It has been shown that the damper response can be satisfactorily predicted with this model.

scholarly journals Predicting Crime and Other Uses of Neural Networks in Police Decision Making

2021 ◽  
Vol 12 ◽  
Author(s):  
Steven Walczak
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Making ◽  
Law Enforcement ◽  
Prediction Models ◽  
Network Models ◽  
Time Of Day ◽  
Neural Network Models ◽  
Police Decision Making ◽  
The Neural Network

Neural networks are a machine learning method that excel in solving classification and forecasting problems. They have also been shown to be a useful tool for working with big data oriented environments such as law enforcement. This article reviews and examines existing research on the utilization of neural networks for forecasting crime and other police decision making problem solving. Neural network models to predict specific types of crime using location and time information and to predict a crime’s location when given the crime and time of day are developed to demonstrate the application of neural networks to police decision making. The neural network crime prediction models utilize geo-spatiality to provide immediate information on crimes to enhance law enforcement decision making. The neural network models are able to predict the type of crime being committed 16.4% of the time for 27 different types of crime or 27.1% of the time when similar crimes are grouped into seven categories of crime. The location prediction neural networks are able to predict the zip code location or adjacent location 31.2% of the time.

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