A fuzzy diagnosis and advice system for optimization of emissions and fuel consumption

Kılağız, Yavuz; Baran, Ahmet; Yildiz, Zerrin; Çetin, Murat

doi:10.1016/j.eswa.2004.10.016

Cited by 12 publications

(5 citation statements)

References 15 publications

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“…This decision-support system is based on a multi-layered weight-oriented feed-forward artificial neural network (Wang, Qu, Liu, & Cheng, 2004;Kilagiza, Barana, Yildizb, & Ç etinc, 2005;Hatzilygeroudis, 2004;Li, 1999). A thorough analysis of the requirements of the system was done.…”

Section: Analysis and Design Of The Clinical Decision Support Systemmentioning

confidence: 99%

A neural network based clinical decision-support system for efficient diagnosis and fuzzy-based prescription of gynecological diseases using homoeopathic medicinal system

Mangalampalli¹,

Mangalampalli²,

Chakravarthy³

et al. 2006

Expert Systems with Applications

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Section: Analysis and Design Of The Clinical Decision Support Systemmentioning

confidence: 99%

A neural network based clinical decision-support system for efficient diagnosis and fuzzy-based prescription of gynecological diseases using homoeopathic medicinal system

Mangalampalli¹,

Mangalampalli²,

Chakravarthy³

et al. 2006

Expert Systems with Applications

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“…Especially important is the possibility to convert subjective non-sharp factors into the corresponding easily manageable and comprehensible numerical scale. More description of the fuzzy set theory and FES can be found in (Zadeh 1965;Kilagiz et al 2005).…”

Section: Fuzzy Expert System (Fes)mentioning

confidence: 99%

Integrating multiple fuzzy expert systems under restricting requirements

Aly¹,

Vrána²

2006

AGR ECON-CZECH

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The multiple, different and specific expertises are often needed in making YES-or-NO (YES/NO) decisions for treating a variety of business, economic, and agricultural decision problems. This is due to the nature of such problems in which decisions are influenced by multiple factors, and accordingly multiple corresponding expertises are required. Fuzzy expert systems (FESs) are widely used to model expertise due to its capability to model real world values which are not always exact, but frequently vague, or uncertain. In addition, they are able to incorporate qualitative factors. The problem of integrating multiple fuzzy expert systems involves several independent and autonomous fuzzy expert systems arranged synergistically to suit a varying problem context. Every expert system participates in judging the problem based on a predefined match between problem context and the required specific expertises. In this research, multiple FESs are integrated through combining their crisp numerical outputs, which reflect the degree of bias to the Yes/No subjective answers. The reasons for independency can be related to maintainability, decision responsibility, analyzability, knowledge cohesion and modularity, context flexibility, sensitivity of aggregate knowledge, decision consistency, etc. This article presents simple algorithms to integrate multiple parallel FES under specific requirements: preserving the extreme crisp output values, providing for null or non-participating expertises, and considering decision-related expert systems, which are true requirements of a currently held project. The presented results provides a theoretical framework, which can bring advantage to decision making is many disciplines, as e.g. new product launching decision, food quality tracking, monitoring of suspicious deviation of the business processes from the standard performance, tax and customs declaration issues, control and logistic of food chains/networks, etc. 

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“…In addition, diagnosis and prediction for fuel consumption is common in the DOE literature. Kilagiz et al (2005) used fuzzy logic to minimize the emission products. Wu and Liu (2012) presented a model based on artificial network to forecast the fuel consumption in an engine.…”

Section: Introductionmentioning

confidence: 99%

More efficiency in fuel consumption using gearbox optimization based on Taguchi method

2014

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Automotive emission is becoming a critical threat to today's human health. Many researchers are studying engine designs leading to less fuel consumption. Gearbox selection plays a key role in an engine design. In this study, Taguchi quality engineering method is employed, and optimum gear ratios in a five speed gear box is obtained. A table of various gear ratios is suggested by design of experiment techniques. Fuel consumption is calculated through simulating the corresponding combustion dynamics model. Using a 95 % confidence level, optimal parameter combinations are determined using the Taguchi method. The level of importance of the parameters on the fuel efficiency is resolved using the analysis of signal-to-noise ratio as well as analysis of variance.

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A fuzzy diagnosis and advice system for optimization of emissions and fuel consumption

Cited by 12 publications

References 15 publications

A neural network based clinical decision-support system for efficient diagnosis and fuzzy-based prescription of gynecological diseases using homoeopathic medicinal system

A neural network based clinical decision-support system for efficient diagnosis and fuzzy-based prescription of gynecological diseases using homoeopathic medicinal system

Integrating multiple fuzzy expert systems under restricting requirements

More efficiency in fuel consumption using gearbox optimization based on Taguchi method

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