scholarly journals Studying the dynamics of nonlinear interaction between enterprise populations

2019 ◽  
Vol 7 (1) ◽  
pp. 44-61
Author(s):  
Hennadii Ivanchenko ◽  
Serhii Vashchaiev
Keyword(s):  
Life Cycle ◽  
Phase Portraits ◽  
Economic Systems ◽  
Self Organizing Map ◽  
Trophic Relations ◽  
The Neural Network ◽  
Characteristic Modes ◽  
Neural Network System ◽  
Acquisition Processes ◽  
Competing Populations

The article highlights the results of a study of the dynamic evolutionary processes of trophic relations between populations of enterprises. A model based on differential equations is constructed, which describes the economic system and takes into account the dynamics of the specific income of competing populations of enterprises in relations of protocooperation, nonlinearity of growth and competition. This model can be used to analyze the dynamics of transient processes in various life cycle scenarios and predict the synergistic effect of mergers and acquisitions. A bifurcation analysis of possible scenarios of dynamic modes of merger and acquisition processes using the neural network system of pattern recognition was carried out. To this end, a Kohonen self-organizing map has been constructed, which recognizes phase portraits of bifurcation diagrams of enterprises life cycle into five separate classes in accordance with the scenarios of their development. As a result of the experimental study, characteristic modes of the evolution of economic systems were revealed, and also conclusions were made on the mechanisms of influence of the external environment and internal structure on the regime of evolution of populations of enterprises.

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.

A MODIFIED KOHONEN SELF-ORGANIZING MAP (KSOM) CLUSTERING FOR FOUR CATEGORICAL DATA

2016 ◽  
Vol 78 (6-13) ◽  
Author(s):  
Azlin Ahmad ◽  
Rubiyah Yusof
Keyword(s):  
Breast Cancer ◽  
Categorical Data ◽  
Data Sets ◽  
Complex Data ◽  
Self Organizing Map ◽  
Distance Calculation ◽  
The Neural Network ◽  
Complex Data Sets ◽  
Separable Problems ◽  
Self Organizing

The Kohonen Self-Organizing Map (KSOM) is one of the Neural Network unsupervised learning algorithms. This algorithm is used in solving problems in various areas, especially in clustering complex data sets. Despite its advantages, the KSOM algorithm has a few drawbacks; such as overlapped cluster and non-linear separable problems. Therefore, this paper proposes a modified KSOM that inspired from pheromone approach in Ant Colony Optimization. The modification is focusing on the distance calculation amongst objects. The proposed algorithm has been tested on four real categorical data that are obtained from UCI machine learning repository; Iris, Seeds, Glass and Wisconsin Breast Cancer Database. From the results, it shows that the modified KSOM has produced accurate clustering result and all clusters can clearly be identified.

Self-Organizing Map Application for Iris Recognition

2014 ◽  
Vol 3 (2) ◽  
pp. 10
Author(s):  
Anna Sedrak Hovakimyan ◽  
Siranush Gegham Sargsyan ◽  
Arshak Nazaryan
Keyword(s):  
Pattern Recognition ◽  
Problem Solving ◽  
Iris Recognition ◽  
Classification Problem ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
The Neural Network ◽  
Human Iris ◽  
Iris Patterns ◽  
Self Organizing

Human iris is  a good subject of biometrical identification, since  iris patterns are unique like fingerprints. Iris is well protected against damage, unlike fingerprints, which can be harder to recognize after years of certain types of manual labor.A problem of iris recognition is considered in the paper. In machine learning, pattern recognition is the assignment of a label to a given input value. Pattern classification is an example of pattern recognition: it attempts to assign each input value to one of a given set of classes. Nowadays various techniques are used for this purpose, and in particular artificial neural networks.For iris recognition problem solving  Kohenen Self Organizing Maps are suggested to use. The software for iris recognition is developed  which is customizable and allows to select the appropriate parameters of the neural network to obtain the most satisfactory results. The developed Self-Organizing Map Library of classes can be used for various kinds of object classification problem solving as well as for any problems suitable to solve with Self-Organizing Maps.

scholarly journals THE ROLE OF EXCHANGE PROPORTIONS IN THE INFLATION AND ECONOMIC CRISES

2014 ◽  
pp. 90-99
Author(s):  
P. V. Ushanov
Keyword(s):  
Life Cycle ◽  
Economic Crisis ◽  
World Market ◽  
Economic Systems ◽  
Economic Crises ◽  
The World ◽  
Modern Economic ◽  
The Impact

The article terminates the author's series of publications on the impact of system of stereotypes of behaviour – "success formula" - on the development of phases of the life cycle of the socio-economic systems [7-11]. The author argues in favor of the view that lifecycle of any object of management is a complex spiral consisting of 10 phases, each of which, in turn, can be regarded as a separate life cycle. Because of the stereotypes of behaviour, prevailing during previous lifecycle phases, a change of lifecycle phases often leads to a crisis and is accompanied by painful correction of exchange proportions. The author motivates his conclusion that the modern economic crisis is caused by the distortion of exchange proportions. The indexes of changes in exchange proportions are proposed to use as an indicator of stability of the world market. Proposals on overcoming the crisis are made.

A Dynamic and Semantically-Aware Technique for Document Clustering in Biomedical Literature

2011 ◽  
pp. 307-319
Author(s):  
Min Song ◽  
Xiaohua Hu ◽  
Illhoi Yoo ◽  
Eric Koppel
Keyword(s):  
Neural Network ◽  
Information Retrieval ◽  
Cell Structure ◽  
Document Clustering ◽  
Biomedical Literature ◽  
Self Organizing Map ◽  
Context Sensitive ◽  
Growing Cell ◽  
The Neural Network ◽  
Structure Algorithm

As an unsupervised learning process, document clustering has been used to improve information retrieval performance by grouping similar documents and to help text mining approaches by providing a high-quality input for them. In this paper, the authors propose a novel hybrid clustering technique that incorporates semantic smoothing of document models into a neural network framework. Recently, it has been reported that the semantic smoothing model enhances the retrieval quality in Information Retrieval (IR). Inspired by that, the authors developed and applied a context-sensitive semantic smoothing model to boost accuracy of clustering that is generated by a dynamic growing cell structure algorithm, a variation of the neural network technique. They evaluated the proposed technique on biomedical article sets from MEDLINE, the largest biomedical digital library in the world. Their experimental evaluations show that the proposed algorithm significantly improves the clustering quality over the traditional clustering techniques including k-means and self-organizing map (SOM).

Forecasting Emerging Market Indexes with Neural Networks

2011 ◽  
pp. 80-101 ◽  
Author(s):  
Steven Walczah
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Global Knowledge ◽  
Index Change ◽  
Market Index ◽  
The Neural Network ◽  
Neural Network System ◽  
Homogeneous Models ◽  
Using Data

Forecasting financial time series with neural networks is problematic. Multiple decisions, each of which affects the performance of the neural network forecasting model, must be made, including which data to use and the size and architecture of the neural network system. While most previous research with neural networks has focused on homogenous models, that is, only using data from the single time series to be forecast, the ever more global nature of the world’s financial markets necessitates the inclusion of more global knowledge into neural network design. This chapter demonstrates how specific markets are at least partially dependent on other global markets and that inclusion of heterogeneous market information will improve neural network forecasting performance over similar homogeneous models by as much as 12 percent (i.e., moving from a near 51% prediction accuracy for the direction of the market index change to a 63% accuracy of predicting the direction of the market index change).

Based on RBF Neural Network Modeling of Well Test System

2014 ◽  
Vol 535 ◽  
pp. 606-609
Author(s):  
Jia Tian
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Effective Means ◽  
Test System ◽  
Neural Network Modeling ◽  
Well Test ◽  
Fast Learning ◽  
The Neural Network ◽  
Neural Network System ◽  
Neural Network Toolbox

The Neural Network Toolbox in MATLAB is a powerful instrument of analyzing and designing a neural network system. RBF Neural Network has small computational burden and fast learning rate and is not liable to be trapped by local minimal points etc. So it is an effective means to identify and model a system. In this paper, the Neural Network Toolbox in MATLAB and RBF Neural Network are combined to solve the problem of modeling the pressure in oilfield test well systems and the result is excellent.

On-Track Measurement of Lateral/Vertical Wheel Loads of Running Railway Vehicles Based on the Neural Network

2008 ◽  
Author(s):  
Davood Younesian ◽  
Fahim Javid ◽  
Ebrahim Esmailzadeh
Keyword(s):  
Neural Network ◽  
Probability Distribution ◽  
Contact Forces ◽  
Strain Gauges ◽  
Network Algorithm ◽  
The Neural Network ◽  
Neural Network System ◽  
Neural Network Algorithm ◽  
Vertical Contact ◽  
Electric Signals

A new approach for on-track measurement of the lateral/vertical contact forces is presented in this paper. The proposed method is based on measurement of the strain at two sides of the wheel web. Electric signals generated by the strain gauges are fed into a neural network algorithm in order to predict the lateral/vertical contact forces. Feed-forward technique is used in the neural network algorithm. A sensitivity analysis has been carried out to find the best position for the strain gauges. A dynamic model of a freight wagon is provided and a variety of numerical simulations are performed to obtain the probability distribution of the lateral and vertical contact forces. The obtained probability distribution function is then utilized to generate lateral/vertical contact forces within the practical range. In order to train the neural network system, the generated contact forces are applied to the wheel flange and the strain signals are obtained. More than 100 configurations are fed into the system in order to train it. Reliability, accuracy and sensitivity of the proposed measurement system are then investigated.

scholarly journals Prediction of fragmentation of kidney stones: A statistical approach from NCCT images

2016 ◽  
Vol 10 (7-8) ◽  
pp. 237 ◽  
Author(s):  
Krishna Moorthy ◽  
Meenakshy Krishnan
Keyword(s):  
Neural Network ◽  
Renal Stone ◽  
Statistical Approach ◽  
Region Of Interest ◽  
Stone Disease ◽  
True Negative ◽  
The Neural Network ◽  
Neural Network System ◽  
Percent Accuracy ◽  
And Training

<p><strong>Introduction:</strong> We sought to develop a system to predict the fragmentation of stones using non-contrast computed tomography (NCCT) image analysis of patients with renal stone disease.</p><p><strong>Methods:</strong> The features corresponding to first order statistical (FOS) method were extracted from the region of interest in the NCCT scan image of patients undergoing extracorporeal shockwave lithotripsy (ESWL) treatment and the breakability was predicted using neural network.</p><p><strong> Results:</strong> When mean was considered as the feature, the results indicated that the model developed for prediction had sensitivity of 80.7% in true positive (TP) cases. The percent accuracy in identifying correctly the TP and true negative (TN) cases was 90%. TN cases were identified with a specificity of 98.4%.</p><p><strong>Conclusions:</strong> Application of statistical methods and training the neural network system will enable accurate prediction of the fragmentation and outcome of ESWL treatment.</p>

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