Cluster analysis and neural network-based metamodeling of priority rules for dynamic sequencing

2015 ◽  
Vol 29 (1) ◽  
pp. 69-91 ◽  
Author(s):  
A. S. Xanthopoulos ◽  
D. E. Koulouriotis
Keyword(s):  
Neural Network ◽  
Cluster Analysis ◽  
Priority Rules ◽  
Dynamic Sequencing

Analyzing the "Strategy of Socio-Economic Development of the Chuvash Republic until 2035" content

2021 ◽  
Vol 20 (2) ◽  
pp. 299-324
Author(s):  
Valerii V. SMIRNOV
Keyword(s):  
Neural Network ◽  
Cluster Analysis ◽  
Economic Development ◽  
Growth Rates ◽  
Systems Approach ◽  
Cultural Institutions ◽  
Socio Economic Development ◽  
Chuvash Republic ◽  
Number Of Visits ◽  
And Cluster Analysis

Subject. The article addresses a strategy for socio-economic development of the region. Objectives. The purpose is to define contradictions and opportunities to achieve the expected results of the "Strategy of Socio-economic Development of the Chuvash Republic until 2035". Methods. The study rests on the systems approach, using the methods of statistical, neural network, and cluster analysis. Results. The statistical analysis of trends in expected outcomes of the Strategy implementation enabled to build a median hierarchy of their growth rates, where the indicator of an increase in the number of visits to cultural institutions is a priority in setting the goals for the region’s development. The neural network analysis demonstrates the importance of the growth rate of real accrued wages of employees of organizations and the insignificant value of the increase in the number of visits to cultural institutions for effective achievement of all objectives of the Strategy. The cluster analysis shows the importance of growth rates of indicators of the proportion of organizations engaged in technological innovations, and the proportion of shipped innovative products. The analysis of growth rates of GRP and expenditures of the consolidated budget of the Chuvash Republic reveals a decrease in the cyclical lag of the first dynamic pattern from the second one. Conclusions. For the Chuvash Republic, a strategic priority is to overcome the GRP growth limit through the innovative development of backbone areas of economic activity.

Typhoon Economic Loss Prediction in China by Applying General Regression Neural Network and Hierarchical Cluster Analysis

2014 ◽  
Vol 9 (1) ◽  
pp. 42-47
Author(s):  
Bo Cheng ◽  
◽  
Ling Cheng ◽  
Lingmin Jiang ◽  
Keyword(s):  
Neural Network ◽  
Cluster Analysis ◽  
Hierarchical Cluster Analysis ◽  
Hierarchical Cluster ◽  
Economic Loss ◽  
Kernel Functions ◽  
General Regression Neural Network ◽  
Damage Prediction ◽  
Loss Prediction ◽  
General Regression

Natural disasters may cause extreme damage and enormous economic loss. It is important to look for efficient and precise damage prediction models using neural networks, which are increasingly used in many applications. One challenge of developing such a damage prediction model is its limited amount of available data. We therefore chose to predict typhoon damage loss based on a general regression neural network (GRNN). The GRNN is able to converge to kernel functions of data with limited training samples available. This paper investigates a GRNN-based neural network and introduces a loss prediction index. The proposed GRNN structure gives an improved prediction performance with a normalized mean squared error of 0.0071 and a correlation of 0.9321. According to prediction results of economic loss, 30 typhoons have been grouped into five categories by hierarchical cluster analysis. Due to its simplicity and fast-converging features, this scheme is suitable for practical, simple but robust typhoon damage prediction.

Feedback and Sensitivity Analysis for Mass Data from Transverse Joints Monitoring System of Super Arch Dam in Sino Mainland

2013 ◽  
Vol 663 ◽  
pp. 198-201
Author(s):  
Ya Jun Wang ◽  
Zheng Zuo ◽  
Xin Jun Yan ◽  
Peng Wei Guo
Keyword(s):  
Neural Network ◽  
Cluster Analysis ◽  
Sensitivity Analysis ◽  
Arch Dam ◽  
Fuzzy Cluster ◽  
Monitoring Data ◽  
Elman Network ◽  
Fuzzy Cluster Analysis ◽  
Diagnosis Accuracy ◽  
Som Neural Network

The fuzzy cluster analysis for monitoring data, based on SOM neural network, was applied to study the working behaviors of transverse joints of the super arch dam during construction phase. The parental samples were identified with the classification results of fuzzy-cluster researches. Thereby, the Elman network was used to diagnose test samples. Introducing the transverse joints monitoring data from super arch dam as an example, the results show that the proposed method has superb applicability and stability in both of error analysis and diagnosis accuracy.

Construction and Application of Hierarchical Knowledge Granularity

2010 ◽  
Vol 143-144 ◽  
pp. 717-721
Author(s):  
Chun Feng Liu ◽  
Li Feng
Keyword(s):  
Neural Network ◽  
Cluster Analysis ◽  
Computational Complexity ◽  
Granular Computing ◽  
Attribute Reduction ◽  
Optimization Theory ◽  
Hierarchy Analysis ◽  
Speed Up ◽  
Construction Algorithms ◽  
Knowledge Granularity

As one aspect of granular computing, hierarchical knowledge granularity can speed up solution, and reduce computational complexity. This paper describes the structure and hierarchy analysis of granularity simply, details the current methods of construction algorithms in granular computing, and emphasizes the performance comparisons of various construction algorithms, and finally reviews the applications of knowledge granularity in rule extraction, attribute reduction, cluster analysis, optimization theory, neural network and fuzzy control and so on.

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