Empirical models based on machine learning techniques for determining approximate reliability expressions

S, Claudio M. Rocco; Muselli, Marco

doi:10.1016/j.ress.2003.10.001

Cited by 27 publications

(11 citation statements)

References 14 publications

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“…To illustrate, consider a system modelled by the network in Figure 1 [36]. In this network, components 1 and 3 are functionally connected in series, components 2 and 4 are functionally connected in series, and their equivalents 1-3 and 2-4 are functionally connected in parallel.…”

Section: Introductionmentioning

confidence: 99%

A reliability model for assessing corporate governance using machine learning techniques

Hernández-Perdomo

Gűney

Rocco

2019

Reliability Engineering & System Safety

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Corporate governance assesses the efficiency and effectiveness of companies' operations and decisions to ensure value creation for shareholders and optimal risk taking. As investors' decision making process largely depends on financial information and corporate reports, transparency is capital for the stability of a company, or even the stability of a country via the corporate sector. This research introduces the system reliability theory to properly model the behavior of companies regarding their corporate governance mechanisms. We propose the assessment of the corporate governance framework by mapping its inputs as components (either in operating or failed state) along with firm characteristics to determine an approximate Structure Function that enables alternatively modeling the functioning of the system, quantifying its reliability and detecting critical components. The advantage of the proposed mapping approach is illustrated using a sample of 1,109 U.S. listed companies during the period 2002-2014, reporting financial and non-financial information as components of the corporate governance system and the return on assets as the system output. The proposed approach is also useful for modeling other non-engineering sub-systems; companies, financial markets or even economies would be exposed to significant risk if these systems do not function properly.

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Section: Introductionmentioning

confidence: 99%

A reliability model for assessing corporate governance using machine learning techniques

Hernández-Perdomo

Gűney

Rocco

2019

Reliability Engineering & System Safety

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“…Recently, some authors have expanded the network reliability body of knowledge by exploring methods applicable to capacity constrained networks. Rocco and Muselli [11] present an approach to analysing a capacity constrained network for reliability through the use of innovative machine-learning methods that significantly reduce the computational burden to obtain a solution. To do so, the authors made use of two machine-learning techniques, decision trees and hamming clusters, to develop approximate reliability expressions for 2TR of a capacitated network.…”

Section: Network Reliabilitymentioning

confidence: 99%

Reliability of capacitated mobile ad hoc networks

Cook

Ramírez-Márquez

2007

Proceedings of the Institution of Mechanical Engineers, Part O:

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Ad hoc networking is used for the flexibility it provides. Its dynamic topology allows employment of the mobile ad hoc wireless network (MAWN) without groundwork because no infrastructure is required. However, these distinctive attributes of ad hoc networking do not conform to a key assumption under which most existing network reliability methods are founded; that network connectivity is known a priori. Therefore, ad hoc networking requires new network reliability assessment methods. Based on the unique characteristics of ad hoc networks, previous research has been focused on the development of methods that consider the probabilistic formation of the network topology and how to embed mobility models within a reliability approximation technique. This paper provides further advances in the area of MAWN reliability by removing the previous assumptions that link status is solely determined by node separation distance, resulting in a binary link behaviour. This paper proposes the use of a mathematical model for link capacity combined with a Monte Carlo simulation of the demand upon each link and each node's mobility. This new extension provides the ability to consider additional design parameters and their impact on reliability.

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“…As an example, Rocco and Muselli (2004) developed network reliability methods using machine learning techniques to account for 2TR when node and link capacity are incorporated into the model. Further, Ramirez-Marquez and Coit (2004) proposed a heuristic method to address both multi-state and capacitated network reliability.…”

Section: Literature Reviewmentioning

confidence: 99%

Recent Research on the Reliability Analysis Methods for Mobile Ad-hoc Networks

2007

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The Mobile Ad-hoc Wireless Network (MAWN) is a network schema that does not require the infrastructure items (e.g., cellular towers) of typical networks. The fl exibility this offers has led to the proliferation of this network schema. However, this unique attribute of ad-hoc networking also violates the base assumptions on which existing network reliability methods are founded. That is, that the confi guration of the network is known a priori. This paper will describe methods being developed to fi ll the void left by existing techniques. This paper will also describe the utilization of these methods in a systems engineering construct.

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Empirical models based on machine learning techniques for determining approximate reliability expressions

Cited by 27 publications

References 14 publications

A reliability model for assessing corporate governance using machine learning techniques

A reliability model for assessing corporate governance using machine learning techniques

Reliability of capacitated mobile ad hoc networks

Recent Research on the Reliability Analysis Methods for Mobile Ad-hoc Networks

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