A knowledge-based fuzzy expert system to analyse degraded terrain

2009 ◽  
Vol 36 (2) ◽  
pp. 2459-2472 ◽  
Author(s):  
Dieter D. Genske ◽  
Klemens Heinrich
2012 ◽  
Vol 52 (No. 10) ◽  
pp. 456-460 ◽  
Author(s):  
S. Aly ◽  
I. Vrana

Efficient modeling of the artificial intelligence tools has become a necessity in order to cut down the development and maintenance cost associated with building application systems in the business, industrial and agriculture sectors that are frequently amendable to sudden unexpected environmental and economic conditions changes. This can be accomplished through developing an efficient modeling language which exploits the beneficial features of the emerging object-oriented technology. This research is aimed at reviewing the recent scientific aspects of the research concerning conceptual modeling of fuzzy knowledge-based system, which exhibits a large extent of applicability in last few decades due to its capability to deal with vagueness, uncertainty and subjectivity, those are inherent in real world problems. The most recent researches and applications of fuzzy expert system are surveyed. The existing knowledge modeling techniques are reviewed and the prominent ones are pinpointed. This paper is intended to identify the main and common bottlenecks of the existing knowledge modeling tools to overcome it in developing a reliable conceptual model of fuzzy expert system. 


2020 ◽  
Vol 16 (01) ◽  
pp. 163-176
Author(s):  
Juthika Mahanta ◽  
Subhasis Panda

A fuzzy expert system (FES) for the prediction of prostate cancer (PC) is prescribed in this paper. Age, prostate-specific antigen (PSA), prostate volume (PV) and [Formula: see text] Free PSA ([Formula: see text]FPSA) are fed as inputs into the FES and prostate cancer risk (PCR) is obtained as the output. Using knowledge-based rules in Mamdani type inference method the output is calculated. If PCR [Formula: see text], then the patient shall be advised to go for a biopsy test for confirmation. The efficacy of the designed FES is tested against a clinical dataset. The true prediction for all the patients turns out to be [Formula: see text] whereas only for positive biopsy cases it rises to [Formula: see text]. This simple yet effective FES can be used as supportive tool for decision-making in medical diagnosis.


2011 ◽  
Vol 48-49 ◽  
pp. 519-522 ◽  
Author(s):  
Yang Lan Ou

Locating the causes of malfunctions in complex energy systems is an extremely difficult task, more than one fault mode may produce similar and possibly undistinguishable patterns of effects. This paper shows how fuzzy expert systems can exploit the available measurements from the data acquisition system to identify different component and sensor fault modes. Real sensor data (mass flow rates, pressures, temperatures, and key operating parameters) are compared with the expected values of the same quantities that are calculated using numerical models of local subsystems. The final objective is to verify the existence of some patterns of these attributes that univocally identify the considered fault modes. These patterns are then implemented as the set of rules forming the knowledge based on fuzzy expert system.


2001 ◽  
Vol 06 (02) ◽  
Author(s):  
C.A Magni ◽  
G. Mastroleo ◽  
G. Facchinetti

1992 ◽  
Vol 57 (10) ◽  
pp. 2125-2134 ◽  
Author(s):  
Petr Stehlík ◽  
František Babinec

An application of a fuzzy expert system intended for estimating some parameters of steam reforming can also be one of the examples of an ever increasing utilization of expert systems in practice. The present contribution deals with the method making use of a verified mathematical model for simulating thermal chemical processes in reforming furnace radiation chamber in order to create knowledge base. This base includes linguistic values of selected independent and dependent variable quantities. Examples given illustrate an evaluation of dependent variable quantities (methane conversion into carbon dioxide and monoxide, reaction tube service life) by means of the said expert system based on queries.


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