An optimization neural network model with time-dependent and lossy dynamics

Heszberger, Zalán; Bı́ró, József

doi:10.1016/s0925-2312(01)00656-7

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…(1) We note that the total final private sector expenditure amounted to $ 13.443 billion in 2004, rising to $ 61.56 billion in 2010, then increased significantly in 2019 to reach $ 146.807 billion. US dollars, which indicates the effect of goods and services on domestic demand; besides the increase in population, the Iraqi population reached 26.340 million in 2003, bringing the population to about 33.330 million people in 2011 [12]. Then it will grow to nearly 40 million people in 2019.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

A Study on Equity and Distribution of National Income and Poverty Method

Böttcher

2021

JEBI

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The study offers an analysis of two important indicators: the equity of the distribution of national income and poverty, using the Gini coefficient, as it gives a numerical measure of the equity of the distribution. The last decades in Iraq and Egypt have witnessed widening the gap between different income groups due to several economic, political, and social factors. Therefore, the research focused on analyzing and measuring the phenomenon of poverty and its relation to the equity of income distribution for a sample of developing countries based on the annual statistical bulletins of the World Bank. Research has increased the phenomenon of poverty. In Iraq, based on national income per capita and total household consumption data using the Gini coefficient, the research concluded with a set of conclusions. The most important is the widening of the gap between per capita consumption and total family consumption increase in national income in Iraq. About the analysis of the phenomenon of Poverty in Egypt, the great disparity is also evident. In the poverty gap between national income per capita and total household consumption, despite the increase in national income in both countries, indicating low national income equity in both countries, and this is the result of the high cost of living and low real incomes, as well as the depreciation of the local currency. Therefore, the research problem boils down to whether the increase in the national income rate reflects positively in reducing inequality in the income distribution of the two countries and reduces the phenomenon of poverty.

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Section: Theoretical Frameworkmentioning

confidence: 99%

A Study on Equity and Distribution of National Income and Poverty Method

Böttcher

2021

JEBI

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An optimization neural network model with lossy dynamics and time-varying activation functions

Bı́ró

Heszberger

2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541)

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A Method of Fast Fault Detection Based on ARMA and Neural Network

Yang

2006

2006 6th World Congress on Intelligent Control and Automation

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Current fault detection systems lack the ability to generalize from previously observed patterns to detect even slight variations of unknown faults. In this paper, ARMA model combining with a Hopfield-model net is proposed for describing a approach that provides the ability to generalize from previously observed behavior to recognize future behavior. The approach can be used for fault detection in order to analyze and detect novel anomaly patterns. Meanwhile, a feedback neural network was used to predict the 'expected values' of the anomaly; Using the Neural Network is especially better since it can improve the detection rate without increasing the false positives. Experiments show events and variance of anomaly patterns.

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An optimization neural network model with time-dependent and lossy dynamics

Cited by 3 publications

References 13 publications

A Study on Equity and Distribution of National Income and Poverty Method

A Study on Equity and Distribution of National Income and Poverty Method

An optimization neural network model with lossy dynamics and time-varying activation functions

A Method of Fast Fault Detection Based on ARMA and Neural Network

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