Implementation of an expert system based on fuzzy logic to support stock market decisions

2018 ◽  
Author(s):  
Paweł Wiszenko ◽  
Jan Mulawka
Keyword(s):  
Fuzzy Logic ◽  
Expert System ◽  
Stock Market

ESTIMATING GAS AND ROCK OUTBURST RISK ON THE BASIS OF KNOWLEDGE AND EXPERIENCE – THE EXPERT SYSTEM BASED ON FUZZY LOGIC

2014 ◽  
Vol 59 (1) ◽  
pp. 41-52 ◽  
Author(s):  
Norbert Skoczylas
Keyword(s):  
Fuzzy Logic ◽  
Expert System ◽  
Mining Industry ◽  
Automatic Assessment ◽  
Science Field ◽  
Second Stage ◽  
Two Stages ◽  
Outburst Hazard

Abstract The Author endeavored to consult some of the Polish experts who deal with assessing and preventing outburst hazards as to their knowledge and experience. On the basis of this knowledge, an expert system, based on fuzzy logic, was created. The system allows automatic assessment of outburst hazard. The work was completed in two stages. The first stage involved researching relevant sources and rules concerning outburst hazard, and, subsequently, determining a number of parameters measured or observed in the mining industry that are potentially connected with the outburst phenomenon and can be useful when estimating outburst hazard. Then, the Author contacted selected experts who are actively involved in preventing outburst hazard, both in the industry and science field. The experts were anonymously surveyed, which made it possible to select the parameters which are the most essential in assessing outburst hazard. The second stage involved gaining knowledge from the experts by means of a questionnaire-interview. Subjective opinions on estimating outburst hazard on the basis of the parameters selected during the first stage were then systematized using the structures typical of the expert system based on fuzzy logic.

scholarly journals Modeling the seam strength of denim garments by using fuzzy expert system

2021 ◽  
Vol 16 ◽  
pp. 155892502198897
Author(s):  
Joy Sarkar ◽  
Md Abdullah Al Faruque ◽  
Moni Sankar Mondal
Keyword(s):  
Fuzzy Logic ◽  
Expert System ◽  
Linear Density ◽  
Unit Length ◽  
Coefficient Of Determination ◽  
Sewing Thread ◽  
External Forces ◽  
Fuzzy Logic Expert System ◽  
The Relationship ◽  
Sufficient Strength

The main purpose of this study is to predict and develop a model for forecasting the Seam Strength (SS) of denim garments with respect to the thread linear density (tex) and Stitches Per Inch (SPI) by using a Fuzzy Logic Expert System (FLES). The seam strength is an important factor for the serviceability of any garments. As seams bound the fabric pieces together in a garment, the seams must have sufficient strength to execute this property even in the unexpected severe conditions where the garments are subjected to loads or any additional internal or external forces. Sewing thread linear density and number of stitches in a unit length of the seam are the two of the most important factors that affect the seam strength of any garments. But the relationship among these two specific variables and the seam strength is complex and non-linear. As a result, a fuzzy logic based model has been developed to demonstrate the relationship among these parameters and the developed model has been validated by the experimental trial. The coefficient of determination ( R2) was found to be 0.98. The mean relative error also lies withing acceptable limit. The results have suggested a very good performance of the model in the case of the prediction of the seam strength of the denim garments.

MEDUSA: A Fuzzy Expert System for Medical Diagnosis of Acute Abdominal Pain

1994 ◽  
Vol 33 (05) ◽  
pp. 522-529 ◽  
Author(s):  
M. Fathi-Torbaghan ◽  
D. Meyer
Keyword(s):  
Fuzzy Logic ◽  
Abdominal Pain ◽  
Expert System ◽  
Acute Abdominal Pain ◽  
Medical Knowledge ◽  
Clinical Problem ◽  
Fuzzy Relations ◽  
Diagnostic Decision ◽  
Special Cases ◽  
Hybrid Concept

Abstract:Even today, the diagnosis of acute abdominal pain represents a serious clinical problem. The medical knowledge in this field is characterized by uncertainty, imprecision and vagueness. This situation lends itself especially to be solved by the application of fuzzy logic. A fuzzy logic-based expert system for diagnostic decision support is presented (MEDUSA). The representation and application of uncertain and imprecise knowledge is realized by fuzzy sets and fuzzy relations. The hybrid concept of the system enables the integration of rulebased, heuristic and casebased reasoning on the basis of imprecise information. The central idea of the integration is to use casebased reasoning for the management of special cases, and rulebased reasoning for the representation of normal cases. The heuristic principle is ideally suited for making uncertain, hypothetical inferences on the basis of fuzzy data and fuzzy relations.

scholarly journals Modelling the system behaviour of wet snow avalanches using an expert system approach for risk management on high alpine traffic roads

2005 ◽  
Vol 5 (6) ◽  
pp. 821-832 ◽  
Author(s):  
A. Zischg ◽  
S. Fuchs ◽  
M. Keiler ◽  
G. Meißl
Keyword(s):  
Risk Management ◽  
Fuzzy Logic ◽  
Expert System ◽  
Temporal Variability ◽  
Hazard Index ◽  
Rule Base ◽  
Spatial And Temporal Variability ◽  
Snow Avalanches ◽  
System Behaviour ◽  
Wet Snow

Abstract. The presented approach describes a model for a rule-based expert system calculating the temporal variability of the release of wet snow avalanches, using the assumption of avalanche triggering without the loading of new snow. The knowledge base of the model is created by using investigations on the system behaviour of wet snow avalanches in the Italian Ortles Alps, and is represented by a fuzzy logic rule-base. Input parameters of the expert system are numerical and linguistic variables, measurable meteorological and topographical factors and observable characteristics of the snow cover. Output of the inference method is the quantified release disposition for wet snow avalanches. Combining topographical parameters and the spatial interpolation of the calculated release disposition a hazard index map is dynamically generated. Furthermore, the spatial and temporal variability of damage potential on roads exposed to wet snow avalanches can be quantified, expressed by the number of persons at risk. The application of the rule base to the available data in the study area generated plausible results. The study demonstrates the potential for the application of expert systems and fuzzy logic in the field of natural hazard monitoring and risk management.

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