Fuzzy Logic Essentials

2015 ◽  
pp. 168-208
Keyword(s):  
Fuzzy Logic ◽  
Knowledge Representation ◽  
Fuzzy Inference ◽  
Mathematical Formulation ◽  
Mathematical Expression ◽  
Inference Process

This chapter presents the mathematical formulation of the fuzzy logic essentials and sets and serves as a useful background for entering the mathematical expression of the knowledge representation in the fuzzy world. Particular examples and application spaces are explored for an integrated presentation of the facets of fuzziness, both from a theoretical and practical contemplation. This knowledge could assist the comprehension of the mathematical typology and terminology of the fuzzy concepts, leading to the fuzzy inference process that it is examined in the next chapter.

Fuzzy Logic-Based Inference Systems

2015 ◽  
pp. 209-239
Keyword(s):  
Fuzzy Logic ◽  
Knowledge Representation ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Mathematical Formulation ◽  
Cognitive Map ◽  
Fuzzy Cognitive Map ◽  
Adaptive Networks ◽  
Inference System ◽  
Inference Systems

This chapter presents the mathematical formulation of the fuzzy logic-based inference systems, used as means to infer about the response of ill-conditioned systems, based on the field knowledge representation in the fuzzy world. Particular approaches are explored, e.g., Fuzzy Inference System (FIS), Adaptive Networks-based FIS (ANFIS), Intuitionistic FIS (IFIS) and Fuzzy Cognitive Map (FCM), surfacing their potentialities in modeling applications, such as those in the field of learning, examined in the chapters of Part III that follow.

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.

scholarly journals The idea of fuzzy logic usage in a sheet-based FMEA analysis of mechanical systems

2018 ◽  
Vol 183 ◽  
pp. 03009 ◽  
Author(s):  
Grzegorz Filo ◽  
Joanna Fabiś-Domagała ◽  
Mariusz Domagała ◽  
Edward Lisowski ◽  
Hassan Momeni
Keyword(s):  
Fuzzy Logic ◽  
Risk Analysis ◽  
Fuzzy Sets ◽  
Failure Mode ◽  
Fuzzy Inference ◽  
Logic Model ◽  
Analysis Method ◽  
Inference Process ◽  
Fmea Analysis ◽  
Logic Inference

The main purpose of the work which was carried out and is presented in this paper was to examine the possibility of using fuzzy logic inference for conducting a risk analysis with the help of a sheet-based Failure Mode and Effects Analysis method (FMEA). At the beginning, the main features of the analysed method were presented, with particular emphasis put on the Risk, Priority and Number parameters. Then, a proposal has been made which suggests using Matlab Fuzzy Logic Toolbox package in order to convert the factors into the form of fuzzy sets and to define rules for fuzzy inference process has been made. Finally, the created fuzzy logic model was used to present an example analysis of a turbocharger failure in the fuzzified form.

scholarly journals Vessel Multi-Parametric Collision Avoidance Decision Model: Fuzzy Approach

2021 ◽  
Vol 9 (1) ◽  
pp. 49
Author(s):  
Tanja Brcko ◽  
Andrej Androjna ◽  
Jure Srše ◽  
Renata Boć
Keyword(s):  
Fuzzy Logic ◽  
Collision Avoidance ◽  
Decision Model ◽  
Fuzzy Inference ◽  
Weather Conditions ◽  
Target Vessel ◽  
Planning Tool ◽  
Inference System ◽  
International Regulations ◽  
Input Variables

The application of fuzzy logic is an effective approach to a variety of circumstances, including solutions to maritime anti-collision problems. The article presents an upgrade of the radar navigation system, in particular, its collision avoidance planning tool, using a decision model that combines dynamic parameters into one decision—the collision avoidance course. In this paper, a multi-parametric decision model based on fuzzy logic is proposed. The model calculates course alteration in a collision avoidance situation. First, the model collects input data of the target vessel and assesses the collision risk. Using time delay, four parameters are calculated for further processing as input variables for a fuzzy inference system. Then, the fuzzy logic method is used to calculate the course alteration, which considers the vessel’s safety domain and International Regulations for Preventing Collisions at Sea (COLREGs). The special feature of the decision model is its tuning with the results of the database of correct solutions obtained with the manual radar plotting method. The validation was carried out with six selected cases simulating encounters with the target vessel in the open sea from different angles and at any visibility. The results of the case studies have shown that the decision model computes well in situations where the own vessel is in a give-way position. In addition, the model provides good results in situations when the target vessel violates COLREG rules. The collision avoidance planning tool can be automated and serve as a basis for further implementation of a model that considers the manoeuvrability of the vessels, weather conditions, and multi-vessel encounter situations.

scholarly journals A New Approach for Dynamic Stochastic Fractal Search with Fuzzy Logic for Parameter Adaptation

2021 ◽  
Vol 5 (2) ◽  
pp. 33
Author(s):  
Marylu L. Lagunes ◽  
Oscar Castillo ◽  
Fevrier Valdez ◽  
Jose Soria ◽  
Patricia Melin
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Dynamic Parameter ◽  
Mathematical Concept ◽  
Parameter Adaptation ◽  
Inference Models ◽  
Fractal Particles ◽  
Novel Method ◽  
Dynamic Stochastic ◽  
Stochastic Fractal Search

Stochastic fractal search (SFS) is a novel method inspired by the process of stochastic growth in nature and the use of the fractal mathematical concept. Considering the chaotic stochastic diffusion property, an improved dynamic stochastic fractal search (DSFS) optimization algorithm is presented. The DSFS algorithm was tested with benchmark functions, such as the multimodal, hybrid, and composite functions, to evaluate the performance of the algorithm with dynamic parameter adaptation with type-1 and type-2 fuzzy inference models. The main contribution of the article is the utilization of fuzzy logic in the adaptation of the diffusion parameter in a dynamic fashion. This parameter is in charge of creating new fractal particles, and the diversity and iteration are the input information used in the fuzzy system to control the values of diffusion.

scholarly journals A Soft Computing Approach to Crack Detection and Impact Source Identification with Field-Programmable Gate Array Implementation

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Arati M. Dixit ◽  
Harpreet Singh
Keyword(s):  
Fuzzy Logic ◽  
Soft Computing ◽  
Fuzzy Inference System ◽  
Source Identification ◽  
Field Programmable Gate Array ◽  
Crack Detection ◽  
Fuzzy Inference ◽  
Inference System ◽  
Field Programmable ◽  
Computing Approach

The real-time nondestructive testing (NDT) for crack detection and impact source identification (CDISI) has attracted the researchers from diverse areas. This is apparent from the current work in the literature. CDISI has usually been performed by visual assessment of waveforms generated by a standard data acquisition system. In this paper we suggest an automation of CDISI for metal armor plates using a soft computing approach by developing a fuzzy inference system to effectively deal with this problem. It is also advantageous to develop a chip that can contribute towards real time CDISI. The objective of this paper is to report on efforts to develop an automated CDISI procedure and to formulate a technique such that the proposed method can be easily implemented on a chip. The CDISI fuzzy inference system is developed using MATLAB’s fuzzy logic toolbox. A VLSI circuit for CDISI is developed on basis of fuzzy logic model using Verilog, a hardware description language (HDL). The Xilinx ISE WebPACK9.1i is used for design, synthesis, implementation, and verification. The CDISI field-programmable gate array (FPGA) implementation is done using Xilinx’s Spartan 3 FPGA. SynaptiCAD’s Verilog Simulators—VeriLogger PRO and ModelSim—are used as the software simulation and debug environment.

scholarly journals Anfis to Detect Brain Tumor Using MRI

2018 ◽  
Vol 7 (3.27) ◽  
pp. 209
Author(s):  
Susmita Mishra ◽  
M Prakash ◽  
A Hafsa ◽  
G Anchana
Keyword(s):  
Fuzzy Logic ◽  
Brain Tumor ◽  
Graphical Representation ◽  
Fuzzy Inference ◽  
Early Stage ◽  
Histogram Equalization ◽  
Input Image ◽  
Inference System ◽  
Magnetic Resonance Imaging Mri ◽  
Contrast Stretching

Processing of Magnetic Resonance Imaging(MRI) is one of the widely known best techniques to diagnose brain tumor since it gives better results than ultrasound or X-Ray images. The main objective is to diagnose the presence and extraction of brain tumor using MRI images. Image preprocessing includes contrast stretching, noise filtering and Adaptive Histogram Equalization(AHE). AHE gives a graphical representation of digital image without enhancing above the desired level. The next stage involves transferring the redundant information in input image to reduced set of features is called feature selection and is done by color, shape or texture of an image. Image is segmented using incorporation of Artificial Neural Networks(ANN) and Fuzzy logic called Adaptive Neuro-Fuzzy Inference System(ANFIS) wherein we get the desired output to differentiate tumor affected and normal image with its severity level. Since we deal with uncertainty much more, fuzzy logic serves as a vibrant tool in representing human knowledge as IF-THEN rules. MATLAB has been implemented in detection and extraction of tumor at an early stage. 

scholarly journals Development of a Fuzzy Model for Differentiating Peanut Plant from Broadleaf Weeds Using Image Features

2020 ◽  
Author(s):  
Adel Bakhshipour ◽  
Hemad Zareiforoush
Keyword(s):  
Fuzzy Logic ◽  
Feature Selection ◽  
Decision Trees ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Principal Component ◽  
Image Features ◽  
Fuzzy Classifier ◽  
Inference System ◽  
Peanut Plant

Abstract A combination of decision tree (DT) and fuzzy logic techniques was used to develop a fuzzy model for differentiating peanut plant from weeds. Color features and wavelet-based texture features were extracted from images of peanut plant and its three common weeds. Two feature selection techniques namely Principal Component Analysis (PCA) and Correlation-based Feature Selection (CFS) were applied on input dataset and three Decision Trees (DTs) including J48, Random Tree (RT), and Reduced Error Pruning (REP) were used to distinguish between different plants. In all cases, the best overall classification accuracies were achieved when CFS-selected features were used as input data. The obtained accuracies of J48-CFS, REP-CFS, and RT-CFS trees for classification of the four plant categories namely peanut plant, Velvetleaf, False daisy, and Nicandra, were 80.83%, 80.00% and 79.17% respectively. Along with these almost low accuracies, the structures of the decision trees were complex making them unsuitable for developing a fuzzy inference system. The classifiers were also used for differentiating peanut plant from the group of weeds. The overall accuracies on training and testing datasets were respectively 95.56% and 93.75% for J48-CFS; 92.78% and 91.67% for REP-CFS; and 93.33% and 92.59% for RT-CFS DTs. The results showed that the J48-CFS and REP-CFS were the most appropriate models to set the membership functions and rules of the fuzzy classifier system. Based on the results, it can be concluded that the developed DT-based fuzzy logic model can be used effectively to discriminate weeds from peanut plant in the form of machine vision-based cultivating systems.

scholarly journals Aplikasi Pengambilan Keputusan dengan Metode Tsukamoto pada Penentuan Tingkat Kepuasan pelanggan

CAUCHY ◽  
2015 ◽  
Vol 4 (1) ◽  
pp. 10 ◽  
Author(s):  
Venny Riana Riana Agustin ◽  
Wahyu Henky Irawan
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Weighted Average ◽  
Fuzzy Rules ◽  
Quality Of Services ◽  
Inference System ◽  
A Value ◽  
Fuzzy Logic Analysis

Tsukamoto method is one method of fuzzy inference system on fuzzy logic for decision making. Steps of the decision making in this method, namely fuzzyfication (process changing the input into kabur), the establishment of fuzzy rules, fuzzy logic analysis, defuzzyfication (affirmation), as well as the conclusion and interpretation of the results. The results from this research are steps of the decision making in Tsukamoto method, namely fuzzyfication (process changing the input into kabur), the establishment of fuzzy rules by the general form IF a is A THEN B is B, fuzzy logic analysis to get alpha in every rule, defuzzyfication (affirmation) by weighted average method, as well as the conclusion and interpretation of the results. On customers at the case, in value of 16 the quality of services, the value of 17 the quality of goods, and value of 16 a price, a value of the results is 45,29063 and the level is low satisfaction

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