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.

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 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>

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.

scholarly journals Thermal Conductivity Modeling of Propylene Glycol - Based Nanofluid Using Artificial Neural Network

2018 ◽  
Vol 14 (1) ◽  
pp. 5281-5291 ◽  
Author(s):  
R. A. Mohamed ◽  
D. M. Habashy
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Thermal Conductivity ◽  
Artificial Neural Network ◽  
Propylene Glycol ◽  
Network Model ◽  
Neural Network Model ◽  
Network System ◽  
The Neural Network ◽  
Neural Network System

The article introduces artificial neural network model that simulates and predicts thermal conductivity and particle size of propylene glycol - based nanofluids containing Al2O3 and TiO2 nanoparticles in a temperature rang 20 - 80oc. The experimental data indicated that the nanofluids have excellent stability over the temperature scale of interest and thermal conductivity enhancement for both nanofluid samples. The neural network system was trained on the available experimental data. The system was designed to find the optimal network that has the best training performance. The nonlinear equations which represent the relation between the inputs and output were obtained. The results of neural network model and the theoretical models of the proposed system were performed and compared with the experimental results. The neural network system appears to yield the best fit consistent with experimental data. The results of the paper demonstrate the ability of neural network model as an excellent computational tool in nanofluid field.

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.

scholarly journals Using Models of Collective Neural Networks for Classification of the Input Data Applying Simple Voting

2018 ◽  
pp. 333-339
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Network Architecture ◽  
Input Data ◽  
Bootstrap Method ◽  
Classification Models ◽  
The Neural Network ◽  
Voting Method

This paper deals with the use of neural networks in binary classification problems based on the simple voting method. It specifies that the accuracy of the neural network classification depends both on the choice of the network architecture and on the partitioning of data into training and test sets. It is noted that the process of building a neural network model is probabilistic in nature. To eliminate this drawback and improve the accuracy of classification, the need to combine several models in the form of a collective of neural networks is actualized. To build such a model, it is proposed to use the 0.632-bootstrap method. To aggregate individual solutions formed at the output of each neural network, it is proposed to use a single-choice simple voting. The choice of the model structure in the form of a single-layer Perceptron is justified, and its mathematical model is presented. Using the evaluation data of the functional state of a drunk human as an example, the results of an experimental assessment of the bootstrap error and the accuracy of the neural network model are presented. It is concluded that it is possible to achieve a higher accuracy of classification based on the neural network model when aggregating the results of all bootstrap models using the simple voting method. The accuracy of the constructed model is compared with the accuracy of other classification models. The accuracy of the constructed model was 96.7%, which on average exceeded the accuracy of other classification models by 6.6%. Thus, the neural network collective model is an effective tool for classifying input data using the simple voting method.

scholarly journals A hybrid neural networks and numerical models approach for predicting groundwater abstraction impacts

2008 ◽  
Vol 10 (2) ◽  
pp. 127-137 ◽  
Author(s):  
S. J. Birkinshaw ◽  
G. Parkin ◽  
Z. Rao
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Numerical Model ◽  
Network Model ◽  
Neural Network Model ◽  
River Flow ◽  
Numerical Models ◽  
Groundwater Abstraction ◽  
Output Data ◽  
The Neural Network

A rapid assessment method for evaluating the impacts of groundwater abstraction on river flow depletion has been developed and tested. A hybrid approach was taken, in which a neural network model was used to mimic the results from numerical simulations of interactions between groundwater and rivers using the SHETRAN integrated catchment modelling system. The use of a numerical model ensures self-consistent relationships between input and output data which have a physical basis and are smooth and free of noise. The model simulations required large number of input parameters and several types of time series and spatial output data representing river flow depletions and groundwater drawdown. An orthogonal array technique was used to select parameter values from the multi-dimensional parameter space, providing an efficient design for the neural network training as the datasets are reasonably independent. The efficiency of the neural network model was also improved by a data reduction approach involving fitting curves to the outputs from the numerical model without significant loss of information. It was found that the use of these techniques were essential to develop a feasible method of providing rapid access to the results of detailed process-based simulations using neural networks.

scholarly journals Use of neural networks to predict vault values after implantable collamer lens surgery

2021 ◽  
Author(s):  
Ke Xu ◽  
Xiaoxiao Liu ◽  
Yiming Lei ◽  
Hong Qi ◽  
Chun Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Linear Regression ◽  
Network Model ◽  
Neural Network Model ◽  
Linear Regression Analysis ◽  
Implantable Collamer Lens ◽  
Network Analyses ◽  
The Neural Network ◽  
Input Variables

Abstract Background Appropriate sizing of the implantable collamer lens (ICL) and accurate prediction of the vault are crucial prior to surgery. However, sometimes, the vault value is higher or lower than predicted, necessitating reoperation. The present study aimed to develop neural networks for improving predictions of vault values following ICL implantation based on preoperative biometric data. Methods This retrospective study included 137 eyes of 74 patients with ICLs. Linear regression and neural network analyses were used to examine the relationship between vault values at the 6-month follow-up and preoperative parameters (e.g., ICL characteristics and biometrics). Results Linear regression analysis revealed that vault values were correlated with five variables: ICL size, anterior chamber depth (ACD), angle-to-angle (ATA), white-to-white (WTW), and lens thickness (LT) (adjusted R2 = 0.411). Inclusion of more input variables was associated with better performance in the neural network analysis. The degree of fit when all 11 variables were included in the neural network model was close to 1 (R2 = 0.98). R2 values for the quaternary neural network model enrolling four input variables (ICL size, ATA, ACD, and LT) reached 0.90. Conclusions A neural network equation including the ICL size and biometric parameters of the anterior segment (ATA, ACD, and LT) can be used to predict the postoperative vault, aiding in the selection of an appropriate ICL size and reducing the need for reoperation after surgery.

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