An Application of Fuzzy Expert Systems to Strategic Investments: The Case of Florim S.P.A.

2001 ◽  
Author(s):  
Gisella Facchinetti ◽  
Carlo Alberto Magni ◽  
Giovanni Mastroleo ◽  
Marina Vignola
Keyword(s):  
Expert Systems ◽  
Strategic Investments ◽  
Fuzzy Expert Systems

scholarly journals Integrating multiple fuzzy expert systems under restricting requirements 

2012 ◽  
Vol 52 (No. 4) ◽  
pp. 187-196
Author(s):  
S. Aly ◽  
I. Vrana
Keyword(s):  
Decision Making ◽  
Expert Systems ◽  
Business Processes ◽  
New Product ◽  
Decision Problems ◽  
Food Chains ◽  
Problem Context ◽  
Multiple Factors ◽  
Decision Consistency ◽  
Fuzzy Expert Systems

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. 

scholarly journals Development of a complex method for finding a solution for neuro-fuzzy expert systems

2020 ◽  
Vol 6 (4 (108)) ◽  
pp. 22-31
Author(s):  
Oleg Sova ◽  
Andrii Shyshatskyi ◽  
Dmytro Malitskyi ◽  
Oleksandr Zhuk ◽  
Oleksandr Gaman ◽  
...  
Keyword(s):  
Expert Systems ◽  
Complex Method ◽  
Neuro Fuzzy ◽  
Fuzzy Expert Systems

scholarly journals Development of an improved method for finding a solution for neuro-fuzzy expert systems

2020 ◽  
Vol 5 (4 (107)) ◽  
pp. 35-44
Author(s):  
Olha Salnikova ◽  
Olga Cherviakova ◽  
Oleg Sova ◽  
Ruslan Zhyvotovskyi ◽  
Serhii Petruk ◽  
...  
Keyword(s):  
Expert Systems ◽  
Improved Method ◽  
Neuro Fuzzy ◽  
Fuzzy Expert Systems

Coimplication and its application to fuzzy expert systems

1991 ◽  
Vol 56 (1-3) ◽  
pp. 59-73 ◽  
Author(s):  
Kyung-Whan Oh ◽  
Abraham Kandel
Keyword(s):  
Expert Systems ◽  
Fuzzy Expert Systems

Enhanced Fuzzy Assessment Methodology to Find Overlapping in Membership Function Using K Ratio to Find the Yield of Rice

2020 ◽  
pp. 155-174
Author(s):  
M. Kalpana ◽  
A. V. Senthil Kumar
Keyword(s):  
Fuzzy Logic ◽  
Expert Systems ◽  
Membership Function ◽  
Disease Incidence ◽  
Assessment Methodology ◽  
Fuzzy Expert Systems ◽  
Fuzzy Assessment ◽  
Triangular Membership Function ◽  
1000 Grain Weight ◽  
Hill Number

Fuzzy expert systems are designed based on fuzzy logic and deal with fuzzy sets. Many fuzzy expert systems have been developed for diagnosis. Fuzzy expert systems are developed using fuzzification interface, enhanced fuzzy assessment methodology, and defuzzification interface. Fuzzification helps to convert crisp values into fuzzy values. By applying the enhanced fuzzy assessment methodology for rice, the yield parameters of rice can be diagnosed with number of tillers per hill, number of grains per panicle, and 1000 grain weight. Pest and disease incidence becomes simple for scientists. Enhanced fuzzy assessment methodology for rice uses triangular membership function with Mamdani's inference and K Ratio. Defuzzification interface is adopted to convert the fuzzy values into crisp values. Performance of the system can be evaluated using the accuracy level. Accuracy is the proportion of the total number of predictions that are correct. The proposed algorithm was implemented using MATLAB fuzzy logic tool box to construct fuzzy expert system for rice.

Implementation of Fuzzy Technology in Complicated Medical Diagnostics and Further Decision

Fuzzy Systems ◽  
2017 ◽  
pp. 935-968
Author(s):  
A. B. Bhattacharya ◽  
Arkajit Bhattacharya
Keyword(s):  
Prostate Cancer ◽  
Fuzzy Logic ◽  
Urinary Incontinence ◽  
Expert Systems ◽  
Domestic Animals ◽  
Medical Diagnostics ◽  
Classical Approach ◽  
Medical Work ◽  
Neurological Signs ◽  
Fuzzy Expert Systems

This chapter presents the importance of fuzzy expert systems in the medical field. Efficient and suitable medical work becomes difficult many times without the knowledge of the rules of logic. The chapter highlights the ways of implementing both classical logic and non-classical approach (e.g. temporal and fuzzy logic) in some adverse areas of medical diagnostics. The implementation of fuzzy expert systems is supported by some examples illustrating how indispensable the cognition of logic and showing how applying logic can effectively improve work in medicine. Fuzzy Expert Systems for diagnosis of urinary incontinence, Parkinson's disease, including neurological signs in domestic animals, as well as its implementation for diagnosis of prostate cancer are elaborately discussed.

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