Prediction of the response under impact of steel armours using a multilayer perceptron

García-Crespo, Ángel; Ruíz‐Mezcua, Belén; Fernández-Fdz, D.; Zaera, R.

doi:10.1007/s00521-006-0050-1

Cited by 12 publications

(11 citation statements)

References 11 publications

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“…The present research takes the work realized by Garcia et al [28,29] as a basis, which demonstrated the validity of an ANN to solve simple ballistic impact problems. The current study has generated a new set of ballistic trials with larger dimensions, and has included new environment conditions, that is, new projectile and protection characteristics, to provide a more reliable research scenario.…”

Section: Impact Scenario Parametersmentioning

confidence: 90%

Dealing with limited data in ballistic impact scenarios: an empirical comparison of different neural network approaches

et al. 2009

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In the domain of high-speed impact between solids, the simulation of one trial entails the use of large resources and an elevated computational cost. The objective of this research is to find the best neural network associated with a new problem of ballistic impact, maximizing the quantity of trials available and simplifying their architecture. To achieve this goal, this paper proposes a tuning performance process based on four stages. These stages include existing statistical techniques, a combination of proposals to improve the performance and analyze the influence of each variable. To measure the quality of the different networks, two criteria based on information theory have been incorporated to reflect the fit of the data with respect to their complexity. The results obtained show that the application of an integrated tuning process in this domain permits improvement in the performance and efficiency of a neural network in comparison with different machine learning alternatives

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Section: Impact Scenario Parametersmentioning

confidence: 90%

Dealing with limited data in ballistic impact scenarios: an empirical comparison of different neural network approaches

et al. 2009

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“…This methodology ratifies and amplifies the conclusions obtained in the research of [22,23], extending the number of trials available and introducing new materials for the projectile and the armour. Furthermore, the different stages of the methodology bring new approaches for working efficiently in limited information scenarios, reducing the complexity of the neural model determining the minimum network architecture, and finally, performing an empirical comparison with another automatic learning classifier (SVM).…”

Section: Discussionmentioning

confidence: 52%

“…The present research takes the work realized by Garcia et al [22,23] as a basis, which demonstrated the validity of an ANN to solve simple ballistic impact problems. The current study has generated a new set of ballistic trials with larger dimensions and has included new environment conditions, that is, new projectile and protection characteristics, to provide a more reliable research scenario.…”

Section: Impact Scenario Parametersmentioning

confidence: 90%

“…When elements in the contact between target and projectile are deleted, new contact surfaces are introduced in the contact algorithm to guarantee the continuity between them. This fact avoids numerical problems resulting from this distortion, and the mass loss in the projectile, a typical phenomenon in impact problems, is properly modelled [22].…”

Section: Numerical Simulation In the Impact Problemmentioning

confidence: 99%

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A neural network--based methodology for the recreation of high-speed impacts on metal armours

González‐Carrasco

García-Crespo

Ruíz‐Mezcua

et al. 2011

Neural Comput & Applic

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The prediction of the consequences of a ballistic impact is highly relevant in the advanced material engineering. Traditionally, the solution of this kind of problems was made by means of experimental tests, analytical models or numerical simulations. In this domain, the particularities of the phenomenon at high speed increase the difficulty of the mathematical resolution of the equations associated, and the complexity of the mechanical behaviour of the materials at high strain rates complicates the numerical simulation of the problem. Therefore, this paper describes a neural network-based methodology applied to recreate the ballistic impact phenomenon. The objective of this study is threefold. Firstly, to obtain the most precise prediction possible, minimizing the amount of data used. Secondly, to discover and analyse the influence of each of the variables on the entire neuronal model. Finally, to compare the precision and performance of this methodology with other alternatives of learning machine. The empirical results have shown that the proposed methodology is an interesting approach to reliably solving ballistic impact problems.

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“…Based on 9,25 Training Algorithm Extended BackPropagation -Learning Parameters µ and η for input layer (0.7 and 1) and output layer (0.3 and 0.4)…”

Section: Use Very Small Random Initial Values (As Much As Possible)mentioning

confidence: 99%

SEffEst: Effort estimation in software projects using fuzzy logic and neural networks

González‐Carrasco¹,

Colomo‐Palacios²,

López-Cuadrado³

et al. 2012

IJCIS

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Academia and practitioners confirm that software project effort prediction is crucial for an accurate software project management. However, software development effort estimation is uncertain by nature. Literature has developed methods to improve estimation correctness, using artificial intelligence techniques in many cases. Following this path, this paper presents SEffEst, a framework based on fuzzy logic and neural networks designed to increase effort estimation accuracy on software development projects. Trained using ISBSG data, SEffEst presents remarkable results in terms of prediction accuracy.

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Prediction of the response under impact of steel armours using a multilayer perceptron

Cited by 12 publications

References 11 publications

Dealing with limited data in ballistic impact scenarios: an empirical comparison of different neural network approaches

Dealing with limited data in ballistic impact scenarios: an empirical comparison of different neural network approaches

A neural network--based methodology for the recreation of high-speed impacts on metal armours

SEffEst: Effort estimation in software projects using fuzzy logic and neural networks

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