scholarly journals Modeling Exchange Rates With Neural Networks

2011 ◽  
Vol 14 (1) ◽  
pp. 1 ◽  
Author(s):  
A. M. M. Jamal ◽  
Cuddalore Sundar
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Exchange Rates ◽  
Network Model ◽  
Neural Network Model ◽  
Econometric Model ◽  
Short Term ◽  
Bilateral Exchange ◽  
The Neural Network ◽  
The U.S

<span>This paper applies the neural network model to forecast bilateral exchange rates between the U.S. and Germany and U.S. and France. The predictions from the neural network model were compared to those based on a standard econometric model. The results suggest that the neural network model may have some advantages when frequent short term forecasts are needed.</span>

Credit Scoring Using Supervised and Unsupervised Neural Networks

2002 ◽  
pp. 154-166 ◽  
Author(s):  
David West ◽  
Cornelius Muchineuta
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Support ◽  
Network Model ◽  
Neural Network Model ◽  
Credit Scoring ◽  
Self Organizing Map ◽  
Subprime Lending ◽  
The Neural Network ◽  
Combined Use

Some of the concerns that plague developers of neural network decision support systems include: (a) How do I understand the underlying structure of the problem domain; (b) How can I discover unknown imperfections in the data which might detract from the generalization accuracy of the neural network model; and (c) What variables should I include to obtain the best generalization properties in the neural network model? In this paper we explore the combined use of unsupervised and supervised neural networks to address these concerns. We develop and test a credit-scoring application using a self-organizing map and a multilayered feedforward neural network. The final product is a neural network decision support system that facilitates subprime lending and is flexible and adaptive to the needs of e-commerce applications.

APPLYING NEURAL NETWORKS TO SOFTWARE RELIABILITY ASSESSMENT

2010 ◽  
Vol 17 (04) ◽  
pp. 313-329 ◽  
Author(s):  
NORMAN SCHNEIDEWIND
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Software Reliability ◽  
Likelihood Estimation ◽  
Time To Failure ◽  
The Neural Network ◽  
Method Of Maximum Likelihood ◽  
Network Method

We adapt concepts from the field of neural networks to assess the reliability of software, employing cumulative failures, reliability, remaining failures, and time to failure metrics. In addition, the risk of not achieving reliability, remaining failure, and time to failure goals are assessed. The purpose of the assessment is to compare a criterion, derived from a neural network model, for estimating the parameters of software reliability metrics, with the method of maximum likelihood estimation. To our surprise the neural network method proved superior for all the reliability metrics that were assessed by virtue of yielding lower prediction error and risk. We also found that considerable adaptation of the neural network model was necessary to be meaningful for our application – only inputs, functions, neurons, weights, activation units, and outputs were required to characterize our application.

scholarly journals Neural network method as means of processing experimental data on grain crop yields

2020 ◽  
Vol 161 ◽  
pp. 01031
Author(s):  
Aleksandr Nikiforov ◽  
Aleksei Kuchumov ◽  
Sergei Terentev ◽  
Inessa Karamulina ◽  
Iraida Romanova ◽  
...  
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Neural Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Crop Yields ◽  
Winter Triticale ◽  
The Neural Network ◽  
Grain Crop ◽  
Scientific Results

In the work based on agroecological and technological testing of varieties of grain crops of domestic and foreign breeding, winter triticale in particular, conducted on the experimental field of the Smolensk State Agricultural Academy between 2015 and 2019, we present the methodology and results of processing the experimental data used for constructing the neural network model. Neural networks are applicable for solving tasks that are difficult for computers of traditional design and humans alike. Those are processing large volumes of experimental data, automation of image recognition, approximation of functions and prognosis. Neural networks include analyzing subject areas and weight coefficients of neurons, detecting conflict samples and outliers, normalizing data, determining the number of samples required for teaching a neural network and increasing the learning quality when their number is insufficient, as well as selecting the neural network type and decomposition based on the number of input neurons. We consider the technology of initial data processing and selecting the optimal neural network structure that allows to significantly reduce modeling errors in comparison with neural networks created with unprepared source data. Our accumulated experience of working with neural networks has demonstrated encouraging results, which indicates the prospects of this area, especially when describing processes with large amounts of variables. In order to verify the resulting neural network model, we have carried out a computational experiment, which showed the possibility of applying scientific results in practice.

scholarly journals A neural network model for short term prediction of surface ozone at Pone

MAUSAM ◽  
2021 ◽  
Vol 50 (1) ◽  
pp. 91-98
Author(s):  
P. GUHATHAKURTA
Keyword(s):  
Neural Network ◽  
Air Pollution ◽  
Network Model ◽  
Neural Network Model ◽  
Surface Ozone ◽  
Anthropogenic Activity ◽  
Short Term ◽  
Term Prediction ◽  
The Neural Network ◽  
Short Term Prediction

A new method for short term prediction of air pollution is presented using the neural network technique, Due to increase in industrial and anthropogenic activity, air pollution is a serious subject of concern today, Surface ozone can be considered as a representative of total atmospheric oxidants and of air pollution, A three layer neural network model using the technique of adaptive pattern recognition is developed, The model can predict the mean surface ozone between 12 and 13 hours (hour of maximum concentration), The model can perform well both in training and independent periods, The classical methods of short term modelling are not reliable enough, The method can also be used for short term prediction of other air pollutants.

scholarly journals Nuclear mass systematics by complementing the Finite Range Droplet Model with neural networks

2019 ◽  
Vol 14 ◽  
pp. 65
Author(s):  
S. Athanassopoulos ◽  
E. Mavrommatis ◽  
K. A. Gernoth ◽  
J. W. Clark
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Predictive Performance ◽  
Finite Range ◽  
Mass Excess ◽  
Nuclear Mass ◽  
Droplet Model ◽  
The Neural Network

A neural-network model is developed to reproduce the differences between experimental nuclear mass-excess values and the theoretical values given by the Finite Range Droplet Model. The results point to the existence of subtle regularities of nuclear structure not yet contained in the best microscopic/phenomenological models of atomic masses. Combining the FRDM and the neural-network model, we create a hybrid model with improved predictive performance on nuclear-mass systematics and related quantities.

Short-Term Power Load Forecasting Based on the Combination of Improved Neural Network Model and Fuzzy Decision Model

2014 ◽  
Vol 543-547 ◽  
pp. 869-872 ◽  
Author(s):  
Jia Ming Li ◽  
Peng Li
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Decision Model ◽  
Fuzzy Decision ◽  
Short Term ◽  
Accurate Analysis ◽  
The Neural Network ◽  
Power Load ◽  
Fuzzy Decision Model

A fuzzy decision model is built to analyze correlation of historical load datas, and to preprocess original datas, by extracting useful datas contributing to forecasting and removing "bad datas". Then the neural network model (NNM) is established to predict power load value measured at 96 time points, through accurate analysis, the rationality of the model is verified.

scholarly journals Neural Reversible Steganography with Long Short-Term Memory

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Ching-Chun Chang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Low Level ◽  
Long Short Term Memory

Deep learning has brought about a phenomenal paradigm shift in digital steganography. However, there is as yet no consensus on the use of deep neural networks in reversible steganography, a class of steganographic methods that permits the distortion caused by message embedding to be removed. The underdevelopment of the field of reversible steganography with deep learning can be attributed to the perception that perfect reversal of steganographic distortion seems scarcely achievable, due to the lack of transparency and interpretability of neural networks. Rather than employing neural networks in the coding module of a reversible steganographic scheme, we instead apply them to an analytics module that exploits data redundancy to maximise steganographic capacity. State-of-the-art reversible steganographic schemes for digital images are based primarily on a histogram-shifting method in which the analytics module is often modelled as a pixel intensity predictor. In this paper, we propose to refine the prior estimation from a conventional linear predictor through a neural network model. The refinement can be to some extent viewed as a low-level vision task (e.g., noise reduction and super-resolution imaging). In this way, we explore a leading-edge neuroscience-inspired low-level vision model based on long short-term memory with a brief discussion of its biological plausibility. Experimental results demonstrated a significant boost contributed by the neural network model in terms of prediction accuracy and steganographic rate-distortion performance.

scholarly journals Neural network model for recognition of driving strategies and interaction of drivers in traffic conditions

2018 ◽  
Vol 9 (4) ◽  
pp. 153-166
Author(s):  
S.B. Efremov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Traffic Flow ◽  
Network Model ◽  
Neural Network Model ◽  
Self Organizing Map ◽  
Flow Conditions ◽  
Traffic Conditions ◽  
Dangerous Driving ◽  
The Neural Network

The paper presents a neural network model for recognizing driving strategies based on the interaction of drivers in traffic flow conditions. The architecture of the model, based on self-organizing map (SOM), consisting of various neural networks based on RBF (Radial Basis Function). The purpose of this work is to describe the architecture and structure of the neural network model, which allows to recognize the strategic features of driving. Our neural network is able to identify the interaction strategies of cars (drivers) in traffic flow conditions, as well as to identify such behavioral patterns of movement that can be correlated with different types of dangerous driving. From the results of the study, it follows that neural networks of the SOM RBF type are able to recognize and classify the types of interactions in traffic conditions based on modeling the analysis of the trajectories of cars. This neural network showed a high percentage of recognition and clear clustering of similar driving strategies.

scholarly journals Improving prediction of neural networks: a study of tow financial prediction tasks

2004 ◽  
Vol 8 (4) ◽  
pp. 219-233
Author(s):  
Tarun K. Sen ◽  
Parviz Ghandforoush ◽  
Charles T. Stivason
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Prediction Models ◽  
Prediction Performance ◽  
Financial Prediction ◽  
The Neural Network ◽  
Input Variables ◽  
Relationship Of

Neural networks are excellent mapping tools for complex financial data. Their mapping capabilities however do not always result in good generalizability for financial prediction models. Increasing the number of nodes and hidden layers in a neural network model produces better mapping of the data since the number of parameters available to the model increases. This is determinal to generalizabilitiy of the model since the model memorizes idiosyncratic patterns in the data. A neural network model can be expected to be more generalizable if the model architecture is made less complex by using fewer input nodes. In this study we simplify the neural network by eliminating input nodes that have the least contribution to the prediction of a desired outcome. We also provide a theoretical relationship of the sensitivity of output variables to the input variables under certain conditions. This research initiates an effort in identifying methods that would improve the generalizability of neural networks in financial prediction tasks by using mergers and bankruptcy models. The result indicates that incorporating more variables that appear relevant in a model does not necessarily improve prediction performance.

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