A new method on ANN for variance based importance measure analysis of correlated input variables

2012 ◽  
Vol 38 ◽  
pp. 56-63 ◽  
Author(s):  
Wenrui Hao ◽  
Zhenzhou Lu ◽  
Pengfei Wei ◽  
Jun Feng ◽  
Bintuan Wang
Keyword(s):  
New Method ◽  
Importance Measure ◽  
Measure Analysis ◽  
Input Variables

scholarly journals Risk Scenario Generation Based on Importance Measure Analysis

2018 ◽  
Vol 10 (9) ◽  
pp. 3207 ◽  
Author(s):  
Xing Pan ◽  
Lunhu Hu ◽  
Ziling Xin ◽  
Shenghan Zhou ◽  
Yanmei Lin ◽  
...  
Keyword(s):  
Scenario Generation ◽  
Importance Measure ◽  
System Failure ◽  
Generation Process ◽  
Risk Scenarios ◽  
Comparison Results ◽  
Engineering Activity ◽  
Risk Event ◽  
Measure Analysis ◽  
Risk Scenario

A risk scenario is a combination of risk events that may result in system failure. Risk scenario analysis is an important part of system risk assessment and avoidance. In engineering activity-based systems, important risk scenarios are related to important events. Critical activities, meanwhile, mean risk events that may result in system failure. This article proposes these definitions of risk event and risk scenario based on the characteristics of risk in engineering activity-based systems. Under the proposed definitions, a risk scenario framework generated based on importance measure analysis is given, in which critical activities analysis, risk event identification, and risk scenario generation are the three main parts. Important risk events are identified according to activities’ uncertain importance measure and important risk scenarios are generated on the basis of a system’s critical activities analysis. In the risk scenario generation process based on importance analysis, the importance degrees of network activities are ranked to identify the subject of risk events, so that risk scenarios can be combined and generated by risk events and the importance of scenarios is analyzed. Critical activities are analyzed by Taguchi tolerance design, mathematical analysis, and Monte Carlo simulation methods. Then the degrees of uncertain importance measure of activities are solved by the three methods and these results are compared. The comparison results in the example show that the proposed method of uncertain importance measure is very effective for distinguishing the importance level of activities in systems. The calculation and simulation results also verify that the risk events composed of critical activities can generate risk scenarios.

Correlation Analysis in Classifiers

2010 ◽  
pp. 204-218
Author(s):  
Vincent Lemaire ◽  
Carine Hue ◽  
Olivier Bernier
Keyword(s):  
Correlation Analysis ◽  
General Framework ◽  
New Method ◽  
Classification Model ◽  
Data Sets ◽  
Bayesian Classifiers ◽  
Naive Bayesian ◽  
Naïve Bayesian ◽  
Predictive Probability ◽  
Input Variables

This chapter presents a new method to analyze the link between the probabilities produced by a classification model and the variation of its input values. The goal is to increase the predictive probability of a given class by exploring the possible values of the input variables taken independently. The proposed method is presented in a general framework, and then detailed for naive Bayesian classifiers. We also demonstrate the importance of “lever variables”, variables which can conceivably be acted upon to obtain specific results as represented by class probabilities, and consequently can be the target of specific policies. The application of the proposed method to several data sets shows that such an approach can lead to useful indicators.

scholarly journals A New Method for Low Power Design of Two-Level Logic Circuits

VLSI Design ◽  
1999 ◽  
Vol 9 (2) ◽  
pp. 147-157
Author(s):  
G. Theodoridis ◽  
S. Theoharis ◽  
D. Soudris ◽  
C. Goutis
Keyword(s):  
Low Power Design ◽  
Logic Circuits ◽  
New Method ◽  
Covering Problem ◽  
Switching Activity ◽  
Network Nodes ◽  
Input Signals ◽  
Input Variables ◽  
Comparison Of The Results ◽  
Switching Activity Reduction

A new method for implementing two-level logic circuits, which exhibit minimal power dissipation, is introduced. Switching activity reduction of the logic network nodes is achieved by adding extra input signals to specific gates. Employing the statistic properties of the primary inputs, a new concept for grouping the input variables with similar features is introduced. Appropriate input variables are chosen for reducing the switching activity of a logic circuit. For that purpose, an efficient synthesis algorithm, which generates the set of all groups of the variables and solves the minimum covering problem for each group is developed. The comparison of the results, produced by the proposed method, and those from ESPRESSO shows that a substantial power reduction can be achieved.

An Improved Control Method to Solve Thin System Problem

2013 ◽  
Vol 791-793 ◽  
pp. 807-812
Author(s):  
Chun Yang Fu ◽  
Qi Wang
Keyword(s):  
Control Problem ◽  
Control Method ◽  
New Method ◽  
Output Variable ◽  
Control Approach ◽  
Loop Method ◽  
Input Variables ◽  
Square Systems ◽  
And Control

Recently, research of non-square systems mainly focuses on fat system, and control approach to thin systems is less. This paper proposes a new method using improved selection control to square the systems and then design IMC controller for thin systems in which the number of output variables exceeds the number of input variables. This proposed method is applied to the control problem in which there is an output variable that is barely impact on the system. Then we use PSO method to seek the parameters. Then we can come to a conclusion that this method has better performance to reducing loop method through simulation. Moreover, this new method is simple and easy to implement.

A new algorithm for variance-based importance measures and importance kernel sensitivity

2012 ◽  
Vol 227 (1) ◽  
pp. 16-27
Author(s):  
Changcong Zhou ◽  
Zhenzhou Lu ◽  
Guijie Li
Keyword(s):  
Numerical Simulation ◽  
Quadratic Polynomial ◽  
Point Estimate ◽  
Importance Measure ◽  
Performance Function ◽  
Point Estimate Method ◽  
Cross Terms ◽  
Distribution Parameters ◽  
Estimate Method ◽  
Input Variables

Variance-based importance measure has proven itself as an effective tool to reflect the effects of input variables on the output. Owing to the desirable properties, researchers have paid lots of attention to improving efficiency in computing a variance-based importance measure. Based on the theory of point estimate, this article proposes a new algorithm, decomposing the importance measure into inner and outer parts, and computing each part with a point estimate method. In order to discuss the impacts on the variance-based importance measure from distribution parameters of input variables, a new concept of kernel sensitivity of the variance-based importance measure is put forward, with solving algorithms respectively, based on numerical simulation and point estimate established as well. For cases where the performance function with independent and normally distributed input variables is expressed by a linear or quadratic polynomial without cross-terms, analytical results of the variance-based importance measure and the kernel sensitivity are derived. Numerical and engineering examples have been employed to illustrate the applicability of the proposed concept and algorithm.

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