Statistical Analysis of the Different Operator Involved in the Fuzzy Inference Process

2006 ◽  
pp. 63-71
Author(s):  
O. Valenzuela ◽  
I. Rojas ◽  
F. Rojas
Keyword(s):  
Statistical Analysis ◽  
Fuzzy Inference ◽  
Inference Process

Statistical analysis of the main parameters in the fuzzy inference process

1999 ◽  
Vol 102 (2) ◽  
pp. 157-173 ◽  
Author(s):  
I. Rojas ◽  
J. Ortega ◽  
F.J. Pelayo ◽  
A. Prieto
Keyword(s):  
Statistical Analysis ◽  
Fuzzy Inference ◽  
Inference Process

ON MODUS PONENS GENERATING FUNCTIONS

2000 ◽  
Vol 08 (01) ◽  
pp. 7-19 ◽  
Author(s):  
ANA PRADERA ◽  
ENRIC TRILLAS ◽  
SUSANA CUBILLO
Keyword(s):  
Generating Function ◽  
Generating Functions ◽  
Fuzzy Inference ◽  
Necessary Conditions ◽  
Modus Ponens ◽  
Inference Process ◽  
Generation Capacity ◽  
Conditional Relations ◽  
Class Of Functions

This paper investigates the use of functions other than t-norms to model the Modus Ponens rule in a fuzzy inference process. For that purpose, new definitions for fuzzy inference related concepts are suggested, that take into account the possibility of using a larger class of functions. In particular, the concept of "Modus Ponens generating function" is revisited, allowing to find out when and where (in which subset of the defined universe) an operator is able to generate the Modus Ponens scheme. In addition, given such an operator, the conditional relations that may be used along with it to model an inference process are found. These results are applied to some common operators, finding their Modus Ponens generation capacity as well as their corresponding residuated fuzzy conditionals. Finally, the relation between an operator's ability to describe the Modus Ponens rule and its conjunctive/disjunctive behaviour is also studied, by means of a series of sufficient and/or necessary conditions relating both concepts.

Hybrid Approach for Sentiment Analysis of Twitter Posts Using a Dictionary-based Approach and Fuzzy Logic Methods

2020 ◽  
Vol 16 (1) ◽  
pp. 116-145 ◽  
Author(s):  
Jamilah Rabeh Alharbi ◽  
Wadee S. Alhalabi
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Fuzzy Systems ◽  
Fuzzy Inference ◽  
Service Providers ◽  
Hybrid Approach ◽  
Cloud Service ◽  
Inference Process ◽  
Huge Amount ◽  
Fuzzy Parameters

Recently, sentiment analysis of social media has become a hot topic because of the huge amount of information that is provided in these networks. Twitter is a popular social media application offers businesses and government the opportunities to share and acquire information. This article proposes a technique that aims at measuring customers' satisfaction with cloud service providers, based on their tweets. Existing techniques focused on classifying sentimental text as either positive or negative, while the proposed technique classifies the tweets into five categories to provide better information. A hybrid approach of dictionary-based and Fuzzy Inference Process (FIP) is developed for this purpose. This direction was selected for its advantages and flexibility in addressing complex problems, using terms that reflect on human behaviors and experiences. The proposed hybrid-based technique used fuzzy systems in order to accurately identify the sentiment of the input text while addressing the challenges that are facing sentiment analysis using various fuzzy parameters.

OBJECTIFICATION OF FUZZY INFERENCE IN DECISION-MAKING PROBLEMS

2021 ◽  
Author(s):  
L. P. Vershinina ◽  
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Statistical Models ◽  
Fuzzy Inference ◽  
Support Systems ◽  
Practical Application ◽  
Inference Process ◽  
Quality Of Information

The basis of modern decision support systems is not so much analytical and statistical models as the practical application of specialists ‘ knowledge. Such systems are based on fuzzy technologies. The quality of decisions made depends on how accurately the quality of information is reflected in the fuzzy inference process. Ways to improve the objectivity of fuzzy inference at the stages of fuzzification, aggregation, activation, and accumulation are proposed.

Developing a Measure of Traffic Congestion: Fuzzy Inference Approach

2002 ◽  
Vol 1802 (1) ◽  
pp. 77-85 ◽  
Author(s):  
Khaled Hamad ◽  
Shinya Kikuchi
Keyword(s):  
Traffic Congestion ◽  
Urban Areas ◽  
Fuzzy Inference ◽  
Supply And Demand ◽  
Travel Speed ◽  
Flow Speed ◽  
Inference Process ◽  
Real World Data ◽  
Total Travel Time ◽  
The Individual

Many measures have been proposed to represent the status of traffic conditions on arterial roadways in urban areas. The debate about what is the most appropriate measure continues. In a contribution to the debate, another approach was offered. Traditionally, two general approaches exist. One is based on the relationship between supply and demand. The other is a measure relative to the most acceptable status of service quality. The latter measure allows the public to relate to their travel experience. In either case, however, derivation of measures of congestion involves uncertainty because of imprecision of the measurement, the traveler’s perception of acceptability, variation in sample data, and the analyst’s uncertainty about causal relations. A measure is proposed that is a composite of two traditional measures, travel speed and delay. In recognition of the uncertainty, a fuzzy inference process was proposed. The inputs are travel speed, free-flow speed, and the proportion of very low speed in the total travel time. These values were processed through fuzzyrule-based inference. The outcome was a single congestion index value between 0 and 1, where 0 is the best condition and 1 is the worst condition. The process was demonstrated using real-world data. The results were compared with those of the Highway Capacity Manual. Although no conclusion can be drawn about the best measure of congestion, the proposed inference process allows the mechanism to combine different measures and also to incorporate the uncertainty in the individual measures so that the composite picture of congestion can be reproduced.

scholarly journals SISTEM PAKAR UNTUK MENDIAGNOSA PENYAKIT PADA IKAN HIAS AIR TAWAR DENGAN FUZZY INFERENCE SYSTEM

JOUTICA ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 194
Author(s):  
Indahsari Dewi Rina ◽  
Dina Komar Lia
Keyword(s):  
Fuzzy Inference ◽  
Expert Knowledge ◽  
Fish Farming ◽  
Ornamental Fish ◽  
Fish Disease ◽  
Inference Process ◽  
Output Variable ◽  
Inference System ◽  
The Media ◽  
Input Variables

One of the main causes of failure in aquaculture activities is due to disease factors. The emergence of disease disorders in fish farming is a biological risk that must always be anticipated. The emergence of diseases in fish is generally the result of complex / unbalanced interactions between the three components in the aquatic ecosystem, namely weak hosts (fish), malignant pathogens and deteriorating environmental quality. Fish cultivators must obtain fast information related to diseases that infect their fish, and how to deal with them. In this study an expert system was created to diagnose ornamental fish disease using the media website, so that it can be used at any time without having to see a doctor / expert. Knowledge base involves 23 symptoms and 5 diseases that are common in freshwater ornamental fish, using a decision table producing 20 Rule. The inference process uses the Tsukamoto fuzzy, the modeling has 23 input variables and 1 output variable. Each input variable has 3 sets and the output variable has 5 sets. The implementation results indicate that the system built can provide diagnostic results with an 85% accuracy rate.

scholarly journals A Bibliometric Statistical Analysis of the Fuzzy Inference System -based Classifiers

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
WenHao Chen ◽  
Md Manjur Ahmed ◽  
Wan Isni Sofiah ◽  
Nor Ashidi Mat Isa ◽  
Nader Ale Ebrahim ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System

The Application of Fuzzy Logic Inference in Construction Vibration Risk Prediction

2011 ◽  
Vol 243-249 ◽  
pp. 6377-6380
Author(s):  
Zhun Zhang ◽  
Yong Bo Yuan
Keyword(s):  
Fuzzy Logic ◽  
Quantitative Analysis ◽  
Fuzzy Inference ◽  
Primary Phase ◽  
Project Manager ◽  
Construction Site ◽  
Inference Process ◽  
Economical Method ◽  
Logic Inference

Construction induced vibration may cause damage of buildings, disturbance of occupants, and sensitive equipments in buildings surrounding a construction site. The prediction of the construction vibration risk is essential for making decisions before the determination of construction method. Previous methods focus on the prediction based on quantitative analysis. The framework of a new method using fuzzy logic to predict the construction vibration risk is proposed in this paper. This method integrates the knowledge and experience from experts and simulates the inference process of human brain using Mamdani fuzzy inference principle. It is a convenient and economical method when used to provide support for project manager making decisions in primary phase of project.

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