Rule-Based Expert System Dedicated for Technological Applications

Chlebus, Edward; Krot, Kamil; Kuliberda, M.

doi:10.1007/978-3-642-21222-2_45

Cited by 5 publications

(4 citation statements)

References 7 publications

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“…All these methodologies, mentioned in the last paper, rely on the transfer of expert knowledge to a complex set of rules. However, transferring expert knowledge is a heuristic process [37]. On the other hand, the mechanism of neural network training is not based on human expertise; however, through a homogeneous structure of neural networks [38], structured knowledge is difficult to extract.…”

Section: Literature Reviewmentioning

confidence: 99%

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A Fuzzy Model of Risk Assessment for Environmental Start-Up Projects in the Air Transport Sector

Polishchuk

Келемен

Gavurová

et al. 2019

IJERPH

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The purpose of this paper is to develop a fuzzy model of the risk assessment for environmental start-up projects in the air transport sector at the stage of business expansion. The model developed for the following software will be a useful tool for the risk decision support system of investment funds in financing environmental start-up projects at the stage of market conquest. Developing a quantitative risk assessment for environmental start-up projects for the air transport sector will increase the resilience of making risk decisions about their financing by the investors. In this paper, a set of 21 criteria for assessing the risk of launching environmental start-up projects in the air transport sector were formulated for the first time by presenting inputs in the form of a linguistic risk assessment and the number of credible expert considerations. The fuzzy risk assessment model, based on expert knowledge, uses linguistic variables, reveals the uncertainty of the input data, and displays a risk assessment with linguistic interpretation. The result of the paper is a fuzzy model that is embedded in a generalized algorithm and tested in an example risk assessment of environmental start-up projects in the air transport sector.

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Section: Literature Reviewmentioning

confidence: 99%

“…The second part of the study uses one of the multi-criteria decision-making methods [35]. The third part of the study uses a selected expert system [35,37]. The knowledge gained in individual research questions enables the relevant methods for these purposes to be compared and to formulate the practical conclusions drawn.…”

Section: Literature Reviewmentioning

confidence: 99%

A Fuzzy Model of Risk Assessment for Environmental Start-Up Projects in the Air Transport Sector

Polishchuk

Келемен

Gavurová

et al. 2019

IJERPH

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“…The problem of evaluating of start-ups can be formalized as a problem of decision making, which is commonly solved using different formalized methodologies like the multi-criteria decision making, expert systems, fuzzy inference systems or their combinations [21,22]. All these methodologies rely on the transfer of expert knowledge into a complex rule-base, however, the transfer of the expert knowledge is a heuristic process [23]. On the other hand, the mechanism of training neural networks does not rely on human expertise, but through a homogeneous structure of neural networks [24] it is difficult to extract the structured knowledge.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Model for Quantitative Assessment of Environmental Start-up Projects in Air Transport

Келемен

Polishchuk

Gavurová

et al. 2019

IJERPH

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The purpose of this paper is to develop an applied fuzzy model of information technology to obtain quantitative estimates of environmental start-up projects in air transport. The developed model will become a useful tool for venture funds, business angels, or crowdfunding platforms for the development of innovative air transport businesses. Obtaining a quantitative estimate of the environmental start-up projects will increase the sustainability of the decision making on the security of financing of such projects by investors. This article develops a fuzzy evaluation model of project start-ups in air transport as an application of our neuro-fuzzy model in a specific air transport environment. The applied model provides output ranking of start-up project teams in air transport based on a four-layer neuro-fuzzy network. The presented model declares the possibilities of the application to solve these economic problems and offers the space for subsequent research focused on its usability in several areas of start-up development, in sectors and processes differentiated. The benefits are also visible for several types of policies, with an emphasis on decision-making processes in regulatory mechanisms to support the state funding in Slovakia, the EU etc.

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“…The inference engine and the knowledge base represent the core of every expert system. The inference engine provides the mathematical means to evaluate the rules in the knowledge base to reach a conclusion about the state of the diagnosed object [58][59][60]. In applications for turbojet engines, the knowledge base contains rules defining the technical state of the turbojet engine based on the outputs from the segmentation algorithm (i.e., the Kohonen neural network).…”

Section: Expert Diagnostic Systemsmentioning

confidence: 99%

Intelligent Thermal Imaging-Based Diagnostics of Turbojet Engines

et al. 2019

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There are only a few applications of infrared thermal imaging in aviation. In the area of turbojet engines, infrared imaging has been used to detect temperature field anomalies in order to identify structural defects in the materials of engine casings or other engine parts. In aviation applications, the evaluation of infrared images is usually performed manually by an expert. This paper deals with the design of an automatic intelligent system which evaluates the technical state and diagnoses a turbojet engine during its operation based on infrared thermal (IRT) images. A hybrid system interconnecting a self-organizing feature map and an expert system is designed for this purpose. A Kohonen neural network (the self-organizing feature map) is successfully applied to segment IRT images of a turbojet engine with high precision, and the expert system is then used to create diagnostic information from the segmented images. This paper represents a proof of concept of this hybrid system using data from a small iSTC-21v turbojet engine operating in laboratory conditions.

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Rule-Based Expert System Dedicated for Technological Applications

Cited by 5 publications

References 7 publications

A Fuzzy Model of Risk Assessment for Environmental Start-Up Projects in the Air Transport Sector

A Fuzzy Model of Risk Assessment for Environmental Start-Up Projects in the Air Transport Sector

Fuzzy Model for Quantitative Assessment of Environmental Start-up Projects in Air Transport

Intelligent Thermal Imaging-Based Diagnostics of Turbojet Engines

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