A new theoretical analysis on organizing principles of water supply networks

Chai, Li He; Li, H. B.

doi:10.2166/aqua.2007.002

Cited by 15 publications

(7 citation statements)

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“…In a self-supervised way, a SOM network extracts structural information from numerical data instead of memorizing it as is shown in Figure 2. The SOM can provide a topological ordering of the classes, thus automatically catching specific features in the input data and organizing them spatially (Chai, Li, 2007). The arithmetic principles of a SOM and our theoretical framework are utterly similar: to introduce the connection of competition and optimization in nonlinear systems.…”

Section: Maximum Entropy Principle and Som Simulationmentioning

confidence: 99%

Comparison of Two Entropy Approaches in Understanding Stock Market Dynamics

Qiao¹

2016

Proceedings of the 5th Economic &Amp; Finance Conference, Miami

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Stock market is a typical complex system with a great number of agents interacting with each other. Recent global financial crisis has shown the importance of understanding the dynamics of the stock market in details with a close look at its complexity. In the complexity science, two entropy approaches have been widely used, i.e. maximum entropy principle and multiscale entropy, and in this paper I define the generalized entropy of the market with fully consideration of the complex interactions among various agents. Following the first approach, the whole market, as an open system, always has an optimization process so that the generalized entropy of the whole stock market is maximal under the given constraints. And I have derived the nonlinear dynamic equation for the stock market is accordingly. Following the second approach, I have been able to identify certain market patterns in different scales for different financial quantities. Using empirical data from both Chinese and US stock markets, simulations, profound discussions and comparison are provided. Thus, a new framework for studying the dynamics of stock market is obtained, which will be very useful for the market investors, analysts and regulators.

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Section: Maximum Entropy Principle and Som Simulationmentioning

confidence: 99%

Comparison of Two Entropy Approaches in Understanding Stock Market Dynamics

Qiao¹

2016

Proceedings of the 5th Economic &Amp; Finance Conference, Miami

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“…According to the connections between potential functions and dynamic evolutional equations, the stochastic evolutionary equations from Eq. ( 7) can be deduced [7][8]:…”

Section: Dynamic Principle Of Maximum Entropymentioning

confidence: 99%

Fractal Mechanism Analysis and Simulation of Information System

Qiao

Chai

2012

AMR

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To find the best design and development model for the information system, this paper discusses the structural formation of the information system and the kinetics of the evolution of information system from the perspective of non-equilibrium statistical mechanics and generalized entropy in the system. Based on this view, physical laws for the evolution dynamics of information system are expressed as the maximum generalized entropy principle (MGEP). Combining with self-organizing feature map algorithm, we have analyzed the effect on the system structure from the exchange between information flows. That is to say, an information system has fractal characteristics. Except for these theories, we have done some numerous simulation on information systems of 10 major cities in China's to verify the fractal characteristics, which has great significant to the guidance of achieving full use of resources, accelerating the process of information society and constructing information systems.

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“…Therefore, the entropy function can be used to describe the uncertainty of complex system based on non-equilibrium statistical mechanics principle which developed by Boltzmann. Let x 1 , x 2 , ⋅⋅⋅ , x n denoted the values of X 1 , X 2 , ⋅⋅⋅ , X n , then the microstructure evolution dynamic equations of lake system which based on maximum entropy principle can be expressed as follows [4][5][6] :…”

Section: Water Quality Assessment Model Based On Maximum Entropy Prin...mentioning

confidence: 99%

Research on the New Assessment Model of Water Source Quality Based on Maximum Entropy Principle

2011

AMR

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Water source quality is the crucial factor to determine the safety and reliability of urban water supply, and the effective water quality assessment is the premise for water source conservation. Taking a lake system as an example, firstly, the maximum entropy principle was used to analyze the evolution mechanism of the structural changes of lake system. And the nonlinear coupling between the different subsystems of lake system was also analyzed. Secondly, the Self-Organizing feature map neural network was used to simulate the dynamic evolution process of lake system. Finally, a new assessment model for water source quality was established based on the maximum entropy principle. Through application it found that this model achieved the quantitative evaluation of water source quality effectively, and surmounted the inadequate that the precious water quality assessment methods had to determination the weights of the different water quality indicators subjectively.

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A new theoretical analysis on organizing principles of water supply networks

Cited by 15 publications

References 0 publications

Comparison of Two Entropy Approaches in Understanding Stock Market Dynamics

Comparison of Two Entropy Approaches in Understanding Stock Market Dynamics

Fractal Mechanism Analysis and Simulation of Information System

Research on the New Assessment Model of Water Source Quality Based on Maximum Entropy Principle

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