Determining the significance of input parameters using sensitivity analysis

Engelbrecht, Andries P.; Cloete, Ian; Żurada, Jacek M.

doi:10.1007/3-540-59497-3_199

Cited by 50 publications

(37 citation statements)

References 2 publications

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“…The ASA method is perfectly valid in these cases, even taking into account the low variability that it provides. In this sense, previous studies corroborate this conclusion [13][14][15][16][17][18][19].…”

Section: Summary and Discussionsupporting

confidence: 84%

“…The application of Analytic Sensitivity Analysis (ASA) has been very useful in such diverse fields as image recognition [13], engineering [14,15], meteorology [16] and medicine [17][18][19]. Nevertheless, this method is limited as far as its number of potential applications, because it is based on the assumption that all of the variables included in the model are quantitative [9].…”

Section: Introductionmentioning

confidence: 99%

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Numeric sensitivity analysis applied to feedforward neural networks

Moreno

Pol

2003

Neural Computing & Applications

111

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During the last 10 years different interpretative methods for analysing the effect or importance of input variables on the output of a feedforward neural network have been proposed. These methods can be grouped into two sets: analysis based on the magnitude of weights; and sensitivity analysis. However, as described throughout this study, these methods present a series of limitations. We have defined and validated a new method, called Numeric Sensitivity Analysis (NSA), that overcomes these limitations, proving to be the procedure that, in general terms, best describes the effect or importance of the input variables on the output, independently of the nature (quantitative or discrete) of the variables included. The interpretative methods used in this study are implemented in the software program Sensitivity Neural Network 1.0, created by our team.

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Section: Summary and Discussionsupporting

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Numeric sensitivity analysis applied to feedforward neural networks

Moreno

Pol

2003

Neural Computing & Applications

111

View full text Add to dashboard Cite

show abstract

“…One of the most common mistakes in simulation based experiments is addressing irrelevant parameters [6]. Generating different scenarios via altering the values of irrelevant parameters, which do not affect the problem for which the simulation is designed, is a main source of redundancy in the total number of runs for this simulator.…”

Section: Relation Between Parameters and Research Problemmentioning

confidence: 99%

On the Choice of Parameter Values for Simulation Based Experiments on Mobile Ad Hoc Networks

Manaseer¹

2016

IJCNS

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Over the course of the last 10 years, research on Mobile Ad Hoc Networks has witnessed high levels of focus. Because of this field being a relatively new topic, most researchers have focused on using simulation as the first choice for performance measurement and evaluation. In order to contribute to the credibility and reliability of research results on Ad Hoc networks, this paper addresses the issue of choosing parameter values that are used as input for simulators used in such experiments by surveying a sample of recently published papers on Ad Hoc Networks. Results show that in the majority of revised papers, the simulation environment is not carefully planned as reflected by the diversity and quality of parameters used.

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“…The fundamental and necessary condition for using models is their credibility, understood as sufficiently good representation (with a sufficiently small error) of all properties of the considered object or phenomenon important for the user [12,16,17].…”

Section: Sensitivity Analysis and Its Measuresmentioning

confidence: 99%

“…7 the curve described by equation (17) and the projection of the verge of the ditch from Fig. 6 on the plane parameter-input variable have been put together.…”

Section: Idea Of a Phase Angle Modulatormentioning

confidence: 99%

Application of Sensitivity Analysis to the Correction of Static Characteristics of a Phase Angle Modulator

Sroka¹

2011

Metrology and Measurement Systems

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The paper presents definitions and relative measures of the system sensitivity and sensitivity of its errors. The model of a real system and model of an ideal measuring system were introduced. It allows to determine the errors of the system. The paper presents also how to use the error sensitivity analysis carried out on the models of the measuring system to the correction of the nonlinearity error of its static characteristic. The corrective function is determined as a relation between the input variable of the tested system and its chosen parameter. The use of the proposed method has been presented on the example of a phase angle modulator. The obtained results have been compared with the results of analytic calculations. The idea of a phase angle modulator is also presented.

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Determining the significance of input parameters using sensitivity analysis

Cited by 50 publications

References 2 publications

Numeric sensitivity analysis applied to feedforward neural networks

Numeric sensitivity analysis applied to feedforward neural networks

On the Choice of Parameter Values for Simulation Based Experiments on Mobile Ad Hoc Networks

Application of Sensitivity Analysis to the Correction of Static Characteristics of a Phase Angle Modulator

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