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 Dynamic versus static neural network model for rainfall forecasting at Klang River Basin, Malaysia

2012 ◽  
Vol 16 (4) ◽  
pp. 1151-1169 ◽  
Author(s):  
A. El-Shafie ◽  
A. Noureldin ◽  
M. Taha ◽  
A. Hussain ◽  
M. Mukhlisin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Network Architecture ◽  
Rainfall Time Series ◽  
Hydrological Process ◽  
Multi Layer Perceptron ◽  
Rainfall Forecasting ◽  
Unseen Data

Abstract. Rainfall is considered as one of the major components of the hydrological process; it takes significant part in evaluating drought and flooding events. Therefore, it is important to have an accurate model for rainfall forecasting. Recently, several data-driven modeling approaches have been investigated to perform such forecasting tasks as multi-layer perceptron neural networks (MLP-NN). In fact, the rainfall time series modeling involves an important temporal dimension. On the other hand, the classical MLP-NN is a static and has a memoryless network architecture that is effective for complex nonlinear static mapping. This research focuses on investigating the potential of introducing a neural network that could address the temporal relationships of the rainfall series. Two different static neural networks and one dynamic neural network, namely the multi-layer perceptron neural network (MLP-NN), radial basis function neural network (RBFNN) and input delay neural network (IDNN), respectively, have been examined in this study. Those models had been developed for the two time horizons for monthly and weekly rainfall forecasting at Klang River, Malaysia. Data collected over 12 yr (1997–2008) on a weekly basis and 22 yr (1987–2008) on a monthly basis were used to develop and examine the performance of the proposed models. Comprehensive comparison analyses were carried out to evaluate the performance of the proposed static and dynamic neural networks. Results showed that the MLP-NN neural network model is able to follow trends of the actual rainfall, however, not very accurately. RBFNN model achieved better accuracy than the MLP-NN model. Moreover, the forecasting accuracy of the IDNN model was better than that of static network during both training and testing stages, which proves a consistent level of accuracy with seen and unseen data.

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 Artificial Neural Network Model For Wind Mill

2021 ◽  
Vol 1 (3) ◽  
pp. 40-48
Author(s):  
Zulfian Azmi
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Input Data ◽  
Euclidean Distance ◽  
Control Case ◽  
Uncertain Variables ◽  
The Neural Network ◽  
Mill Control

Utilization of wind energy sources provides advantages in terms of being environmentally friendly, and it can be energy source is realible. The analysis of wind mill control using Neural Network model for Uncertain Variables or abbreviated as the VTP model is expected to provide a solution in solving the windmill control case. And the Neural Network model for Uncertain Variables uses probability techniques, degree of membership, logical OR function, linear programming and    euclidean distance to reduce the learning process In this research, wind mill control uses variable air pressure and duration of sunshine to determine whether the wind mill is moving or not. Finally, this research tries to analyze windmill control, which in the future is expected to produce a smart wind mill control system. And the Neural Network model for Uncertain Variables can be used to control windmills with the different of input data

scholarly journals Sensitivity Direction Learning with Neural Networks Using Domain Knowledge as Soft Shape Constraints

2021 ◽  
Author(s):  
Kazuyuki Wakasugi
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Network Architecture ◽  
Domain Knowledge ◽  
Neural Network Architecture ◽  
Shape Constraints ◽  
Direction Error ◽  
The Neural Network ◽  
Strong Candidate

If domain knowledge can be integrated as an appropriate constraint, it is highly possible that the generalization performance of a neural network model can be improved. We propose Sensitivity Direction Learning (SDL) for learning about the neural network model with user-specified relationships (e.g., monotonicity, convexity) between each input feature and the output of the model by imposing soft shape constraints which represent domain knowledge. To impose soft shape constraints, SDL uses a novel penalty function, Sensitivity Direction Error (SDE) function, which returns the squared error between coefficients of the approximation curve for each Individual Conditional Expectation plot and coefficient constraints which represent domain knowledge. The effectiveness of our concept was verified by simple experiments. Similar to those such as L2 regularization and dropout, SDL and SDE can be used without changing neural network architecture. We believe our algorithm can be a strong candidate for neural network users who want to incorporate domain knowledge.

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.

Fracture Design Parameters of Middle Carbon Steel in Extra-Low Cycle Bend Torsion Loading

2014 ◽  
Vol 574 ◽  
pp. 342-346
Author(s):  
Hong Yan Duan ◽  
Huan Rong Zhang ◽  
Ming Zheng ◽  
Xiao Hong Wang
Keyword(s):  
Neural Network ◽  
Carbon Steel ◽  
Network Model ◽  
Neural Network Model ◽  
Network Architecture ◽  
Training Data ◽  
Design Parameters ◽  
Ann Model ◽  
Approximate Methods ◽  
The Neural Network

The fracture problems of medium carbon steel under extra-low cycle bend torsion fatigue loading were studied using artificial neural networks (ANN) in this paper. The ANN model exhibited excellent comparison with the experimental results. It was concluded that predicted fracture design parameters by the trained neural network model seem more reasonable compared to approximate methods. It is possible to claim that, ANN is fairly promising prediction technique if properly used. Training ANN model was introduced at first. And then the Training data for the development of the neural network model was obtained from the experiments. The input parameters, the presetting deflection and notch open angle, and the output, the cycle times of fracture were used during the network training. The neural network architecture is designed. The ANN model was developed using back propagation architecture with three layers jump connections, where every layer was connected or linked to every previous layer. The number of hidden neurons was determined according to special formula. The performance of system is summarized at last. In order to facilitate the comparisons of predicted values, the error evaluation and mean relative error are obtained. The result show that the training model has good performance, and the experimental data and predicted data from ANN are in good coherence.

Application of ANN Back-Propagation for Fracture Design Parameters of Middle Carbon Steel in Extra-Low Cycle Bend Torsion Loading

2013 ◽  
Vol 345 ◽  
pp. 272-276 ◽  
Author(s):  
Hong Yan Duan ◽  
You Tang Li ◽  
Zhi Jia Sun ◽  
Yang Yang Zhang
Keyword(s):  
Neural Network ◽  
Carbon Steel ◽  
Network Model ◽  
Neural Network Model ◽  
Network Architecture ◽  
Back Propagation ◽  
Training Data ◽  
Design Parameters ◽  
Ann Model ◽  
The Neural Network

The fracture problems of medium carbon steel (MCS) under extra-low cycle bend torsion loading were studied using artificial neural networks (ANN) in this paper. The training data were used in the formation of training set of ANN. The ANN model exhibited excellent comparison with the experimental results. It was concluded that predicted fracture design parameters by the trained neural network model seem more reasonable compared to approximate methods. It is possible to claim that, ANN is fairly promising prediction technique if properly used. Training ANN model was introduced at first. And then the Training data for the development of the neural network model was obtained from the experiments. The input parameters, notch depth and tip radius of the notch, and the output, the cycle times of fracture were used during the network training. The neural network architecture is designed. The ANN model was developed using back propagation architecture with three layers jump connections, where every layer was connected or linked to every previous layer. The number of hidden neurons was determined according to special formula. The performance of system is summarized at last. In order to facilitate the comparisons of predicted values, the error evaluation and mean relative error are obtained. The result show that the training model has good performance, and the experimental data and predicted data from ANN are in good coherence.

scholarly journals Of Neural Network Model Robustness Through Generating Invariant to Attributes Embeddings

2020 ◽  
Vol 23 (6) ◽  
pp. 1142-1154
Author(s):  
Marat Rushanovich Gazizov ◽  
Karen Albertovich Grigorian
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Input Data ◽  
Domain Adaptation ◽  
High Accuracy ◽  
Important Criterion ◽  
Training Samples ◽  
Model Robustness

Model robustness to minor deviations in the distribution of input data is an important criterion in many tasks. Neural networks show high accuracy on training samples, but the quality on test samples can be dropped dramatically due to different data distributions, a situation that is exacerbated at the subgroup level within each category. In this article we show how the robustness of the model at the subgroup level can be significantly improved with the help of the domain adaptation approach to image embeddings. We have found that application of a competitive approach to embeddings limitation gives a significant increase of accuracy metrics in a complex subgroup in comparison with the previous models. The method was tested on two independent datasets, the accuracy in a complex subgroup on the Waterbirds dataset is 90.3 {y : waterbirds;a : landbackground}, on the CelebA dataset is 92.22 {y : blondhair;a : male}.

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>

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