Evolutionary Analysis for Continuous Search Space

이준성,; 배병규,

doi:10.5391/jkiis.2011.21.2.206

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…For continuous search space problems, conventional GAs, however, tend to converge slowly, and their accuracy can strongly depend on the bit length of the binary code employed. Therefore, the present authors proposed a new GA modified for the real search space [15], and this formulation was used as an optimization engine.…”

Section: Modified Gamentioning

confidence: 99%

An automated CAE system for multidisciplinary structural design: its application to micro accelerometer

Lee

2010

J Mech Sci Technol

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This paper describes a new computer-aided engineering system for multidisciplinary structural design. An automatic finite element mesh generation technique, based on fuzzy knowledge processing and computational geometry, is incorporated into the system, together with one of the commercial finite element analysis codes. A bubble is generated if its distance from existing bubble points is similar to the bubble-spacing function at the point. The bubble-spacing function is well controlled by fuzzy knowledge processing. The Delaunay method is employed as a basic tool for element generation. Automatic finite element generation for three-dimensional MEMS holds great benefits for analyses. An optimum design solution or satisfactory solutions will be automatically searched using the genetic algorithms modified for real search space, together with the automated finite element analysis system. A novel CAE system was developed, and successfully applied to the shape design of a micro accelerometer based on a tunneling current concept.

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Section: Modified Gamentioning

confidence: 99%

An automated CAE system for multidisciplinary structural design: its application to micro accelerometer

Lee

2010

J Mech Sci Technol

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“…The former problem overcomes the same parameter approach mentioned in the Refs. [1,2]. Also, we need to introduce a model that is replaced by a more complex explicit model or an implicit constitutive equation.…”

Section: Introductionmentioning

confidence: 99%

Analysis Methodology of Inelastic Constitutive Parameter Using State Space Method and Neural Network

Lee¹,

2018

IJET

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Background/Objectives: In this paper, we present a method for describing a set of variables of an inelastic constitutive equation based on state space method (SSM) and neural network (NN). The advantage of this method is that it can identify the appropriate parameters. Methods/Statistical analysis: Two NNs based on SSM are proposed. One outputs the ratio of inelastic strain for the internal parameters of the material, and the other is the following state of the inelastic strain ratio and material internal variable. Both NNs were trained and successfully collected using input and output data generated by Chaboche 's model. Findings: As a result, previous NNs have demonstrated their validity as a powerful material model. However, the training data for the proposed NN can't be easily obtained from actual experimental data. Previous neural networks can reproduce the original stress-strain curves. The NNs also produced untrained curves to demonstrate interpolation capabilities. It was also found that the NNs can be estimated to be close to training data. The author defines the implicit constitutive model and proposes the implicit viscous constitutive model using NNs. In modeling, inelastic behavior is generalized in state space representation, and the state space form is constructed by NNs using an input-output data sets. The proposed model was first created from the pseudo-experimental data generated by one of the commonly used configuration models and has been found to be a good replacement for the model. The actual experimental data was then tested, and the proposed model showed the accuracy of its superiority over all existing specified models because the amount of model errors was negligible. Improvements/Applications: The comparison between the NN constitutive laws with the Chaboche's model indicates that the NN constitutive law generated curves with less model errors than the experimental data, thereby indicating the superiority of the neural constitutive law to explicit constitutive laws as a material model.

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Evolutionary Analysis for Continuous Search Space

Cited by 2 publications

References 6 publications

An automated CAE system for multidisciplinary structural design: its application to micro accelerometer

An automated CAE system for multidisciplinary structural design: its application to micro accelerometer

Analysis Methodology of Inelastic Constitutive Parameter Using State Space Method and Neural Network

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