Speed distribution analysis based on maximum entropy principle and Weibull distribution function

2017 ◽  
Vol 36 (5) ◽  
pp. 1480-1489 ◽  
Author(s):  
Muhammad Shoaib ◽  
Imran Siddiqui ◽  
Shafiqur Rehman ◽  
Saif ur Rehman ◽  
Shamim Khan
Keyword(s):  
Distribution Function ◽  
Weibull Distribution ◽  
Maximum Entropy ◽  
Maximum Entropy Principle ◽  
Distribution Analysis ◽  
Weibull Distribution Function ◽  
Speed Distribution ◽  
Entropy Principle

scholarly journals Comparison of Wind Energy Generation Using the Maximum Entropy Principle and the Weibull Distribution Function

Energies ◽  
2016 ◽  
Vol 9 (10) ◽  
pp. 842 ◽  
Author(s):  
Muhammad Shoaib ◽  
Imran Siddiqui ◽  
Shafiqur Rehman ◽  
Saif Rehman ◽  
Shamim Khan ◽  
...  
Keyword(s):  
Distribution Function ◽  
Wind Energy ◽  
Weibull Distribution ◽  
Maximum Entropy ◽  
Maximum Entropy Principle ◽  
Energy Generation ◽  
Weibull Distribution Function ◽  
Wind Energy Generation ◽  
Entropy Principle

Application of Maximum Entropy Model in Distribution of Vehicle Speed

2014 ◽  
Vol 1079-1080 ◽  
pp. 942-945
Author(s):  
Yi Bing Jiang ◽  
Xu Nan Gao ◽  
Ping Han
Keyword(s):  
Maximum Entropy ◽  
Traffic Management ◽  
Maximum Entropy Principle ◽  
Simple Calculation ◽  
Vehicle Speed ◽  
Entropy Model ◽  
Speed Distribution ◽  
Entropy Principle ◽  
The Law ◽  
The Difference

It’s very important for the traffic flow theory, the traffic management and control, and the programming and design of roads to master the law of speed. And the most intuitive form is the speed distribution to describe the law. Based on the maximum entropy principle, this paper proposes a model which can generate the speed distribution curves only with the observed date. And the availability of this method has been proved by the example analysis. The fact has also been found that fitting with maximum entropy distribution is better than with normal distribution in peak hour and is worse than it in off peak hour, but the difference is not great. This model does not need assumptions. Moreover it has a simple calculation and a strong practicability. So this paper makes a contribution to the study of the speed distribution.

An entropy concentration theorem: applications in artificial intelligence and descriptive statistics

1990 ◽  
Vol 27 (2) ◽  
pp. 303-313 ◽  
Author(s):  
Claudine Robert
Keyword(s):  
Artificial Intelligence ◽  
Maximum Entropy ◽  
Large Deviation ◽  
Maximum Entropy Principle ◽  
Statistical Modelling ◽  
Linear Constraints ◽  
Descriptive Statistics ◽  
Entropy Principle ◽  
Maximum Entropy Distribution ◽  
Mathematical Justification

The maximum entropy principle is used to model uncertainty by a maximum entropy distribution, subject to some appropriate linear constraints. We give an entropy concentration theorem (whose demonstration is based on large deviation techniques) which is a mathematical justification of this statistical modelling principle. Then we indicate how it can be used in artificial intelligence, and how relevant prior knowledge is provided by some classical descriptive statistical methods. It appears furthermore that the maximum entropy principle yields to a natural binding between descriptive methods and some statistical structures.

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