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Fuzzy Logic Toolbox Matlab _VERIFIED_ Download 20



The toolbox lets you automatically tune membership functions and rules of a fuzzy inference system from data. You can evaluate the designed fuzzy logic systems in MATLAB and Simulink. Additionally, you can use the fuzzy inference system as a support system to explain artificial intelligence (AI)-based black-box models. You can generate standalone executables or C/C++ code and IEC 61131-3 Structured Text to evaluate and implement fuzzy logic systems.




fuzzy logic toolbox matlab download 20



Create and evaluate interval type-2 fuzzy inference systems with additional membership function uncertainty. Create type-2 Mamdani and Sugeno fuzzy inference systems using the Fuzzy Logic Designer app or using toolbox functions.


This study aimed to use fuzzy logic model to predict various mechanical properties such as compressive strength, flexural strength and post-peak deformation of steel-fiber reinforced concrete. For this purpose, five different dosages of steel fibers (10 kg/m3, 12.5 kg/m3, 15 kg/m3, 17.5 kg/m3 and 20 kg/m3) were used in the mix design. A total of 3 specimens (cylinders and beams) were casted for each fiber dosage and experimentally tested. The experimental results were compared with the simulated results obtained from fuzzy model using fuzzy logic toolbox provided in MATLAB. It was found that the addition of steel fibers improved the flexural strength and post-peak deformation capability of test specimens by almost 12% and 20% respectively. The compressive strength of specimens also increased by 9.15%, when the amount of steel fibers was increased from 10 to 20 kg/m3. Overall, the compressive strength reduced with the addition of fibers as compared to specimens with no fibers. Similarly, it was also observed that fuzzy model can predict the study parameters within acceptable accuracy and the percentage difference between the simulated and experimental values was below 7.5%.


According to [10] and [11], fuzzy logic is very user-friendly and its rules can be very easily written depending on experience. Fuzzy logic modelling allows us to write the relationship between input(s) and output(s) verbally. In fact, Fuzzy logic can be very useful, if the relationship between the input and output is fuzzy. Various researchers [11,12,13], have used fuzzy logic and neural networking to predict various properties of concrete like compressive strength, flexural strength and the impact of various types of additives like fly ash and low lime concretes. For example Saridemir Mustafa [12], studied the impact of adding metakaolin on the compressive strength of cement mortars. The results were also simulated on Fuzzy logic and artificial neural networking. It was found that fuzzy logic can successfully predict the compressive strength of test specimens. Statistical analysis of data gave a root mean square (RMS) value of 0.28. Artificial neural networking also produced acceptable results with RMS value of 1.79. The obtained slope and intercept values for fuzzy model were 0.9764 and 0.5842 respectively. Thamma and Barai [14], used gene expression programming to simulate the effect of Blaine and Alkalis on the compressive strength of cement mortar. It was found that gene expression programming can successfully predict the compressive strength of cement mortar with RMS value of 1.4956. Similarly, another study was conducted by Bekir and Mustafa [13], which involved predicting the compressive strength of concrete using fuzzy logic and artificial neural networking. The obtained RMS value was 2.02 and the linear fit of equation give a slope and intercept value equal to 0.9824 and 0.6354 respectively. According to them, the statistical analysis of results concluded that the results produced by both models were not only satisfactory, but also reliable.


As evident from literature review, different researchers used fuzzy logic modelling for predicting mechanical properties of concrete like compressive and flexural strength by adding different types of admixtures/fibers. In this study, fuzzy logic model was used to predict post-peak deformation capability of test specimens in addition to other mechanical properties such as compressive and flexural strength. In previous studies, fuzzy model was mostly based on experimental data of other researchers. This study involved casting and testing of specimens followed by their modelling in fuzzy logic.


The objective of this experimental study is to evaluate the impact of locally available steel fibers on the mechanical properties of concrete such as compressive strength, flexural strength and post-peak deformation. For this purpose, five different amounts of steel fibers 10 kg/m3, 12.5 kg/m3, 15 kg/m3, 17.5 kg/m3 and 20 kg/m3 were used, respectively. The amount of steel fibers added is also expressed in terms volume of concrete which are 0.13%, 0.16%, 0.24%, 0.42% and 0.84%, respectively. The impact of steel fibers on the afore-mentioned mechanical properties is also predicted by fuzzy logic algorithm. Finally, the experimental results were compared with the simulated data.


In this research study, fuzzy logic-based model was used to simulate the effect of amount of steel fibers on the mechanical properties of concrete i.e. compressive strength, flexural strength and post-peak deformation of test samples. Fuzzy rules were written for this purpose in fuzzy logic toolbox present in MATLAB. Fuzzy logic deals with real world problems in a natural way, where the absence of sharply defined criteria becomes the cause of imprecision. It is very helpful in problems where linguistic uncertainties can play a role in the control mechanism of problem/study concerned [12]. Fuzzy logic was selected based on its ease to develop its rules, simulate the results in a short time and requires a smaller number of experimental data to develop a representative model of study variable [21]. Fuzzy logic has widely been used for modelling various engineering problems like mechanical properties of solder alloys, mechanical properties of concrete, traffic volumes and rainfall prediction models [11], [22].


According to fuzzy logic predicted values of compressive strength for SF specimens, shown in Fig. 9, the compressive strength of specimens increased with the addition of steel fibers. When comparing SF specimens, SF20 had 9.15% higher compressive strength as compared to SF10. However, the experimental results showed that, the addition of steel fibers reduced the compressive strength of test specimens by almost 31%, as compared to specimens without steel fibers (control group).


This study aimed to predicting the effect of locally available steel fibers on the mechanical properties such as compressive strength, flexural strength and post-peak deformation capability of concrete specimens using fuzzy logic. The simulation was carried-out in fuzzy logic toolbox provided in MATLAB and its results were compared with the experimental findings. For this purpose, different volumes of steel fibers 0.13%, 0.16%, 0.24%, 0.42% and 0.84% (10 kg/m3, 12.5 kg/m3, 15 kg/m3, 17.5 kg/m3 and 20 kg/m3) were assimilated in the mix design, and their mechanical properties were determined.


According to simulated results, the addition of steel fibers helped in improving the compressive strength, flexural strength and post-peak deformation capacity of test specimens. The experimental results also supported the findings of fuzzy logic. The compressive strength increased by almost 9.15%, when steel fibers were increased from 0.13 to 0.84%. However, experiments show that the compressive strength of specimens reduced with the addition of steel fibers as compared to specimens without steel fibers (control group).


A fuzzy inference system (FIS) maps given inputs to outputs using fuzzy logic. For example, a typical mapping of a two-input, one-output fuzzy controller can be depicted in a 3-D plot. The plot is often referred to as a control surface plot.


October 13, MATLAB Fuzzy Logic Toolbox Intelligent Control.\n \n \n \n \n "," \n \n \n \n \n \n FUZZY CLUSTERING AND ANFIS 2009\/ \uf0d8 Underfitting : M51 demolm2 \uf0d8 Overfitting: M51: demolm3 \uf0d8 ANFIS \uf0d8 ANFIS GUI \uf0d8 Example1 (training data: clusterdemo.dat)\n \n \n \n \n "," \n \n \n \n \n \n Fuzzy Expert Systems. 2 Motivation On vagueness \u201cEverything is vague to a degree you do not realise until you have tried to make it precise.\u201d Bertrand.\n \n \n \n \n "," \n \n \n \n \n \n Fuzzy Inference (Expert) System\n \n \n \n \n "," \n \n \n \n \n \n Fuzzy Logic Control of Blood Pressure During Anesthesia\n \n \n \n \n "," \n \n \n \n \n \n Neuro-Fyzzy Methods for Modeling and Identification Part 2 : Examples Presented by: Ali Maleki.\n \n \n \n \n "," \n \n \n \n \n \n Fuzzy Logic Toolbox in MATLAB Praktikum 10. example \uf07d We want to buid FIS Mamdani, with this rules : \uf07d 1. If the service is poor or the food is rancid,\n \n \n \n \n "," \n \n \n \n \n \n ESO SDD - Henning Lorch ESO Instrumentation Software Workshop Henning Lorch \u201cReflex\u201d Pipeline Frontend.\n \n \n \n \n "," \n \n \n \n \n \n Introduction to Matlab & Data Analysis 2015 In this tutorial we will: Build a practical application using GUIDE Learn more about graphical user interface.\n \n \n \n \n "," \n \n \n \n \n \n PART 9 Fuzzy Systems 1. Fuzzy controllers 2. Fuzzy systems and NNs 3. Fuzzy neural networks 4. Fuzzy Automata 5. Fuzzy dynamic systems FUZZY SETS AND FUZZY.\n \n \n \n \n "," \n \n \n \n \n \n Matlab_Fuzzy_tool_kit S.C. Chen. \u8ab2\u7a0b\u5b89\u6392 10\/18\/2011: \u2013Matlab Environment Command window.M file and describe file \u2013Using fuzzy toolbox in command window Membership.\n \n \n \n \n "," \n \n \n \n \n \n Universal fuzzy system representation with XML Authors \uff1a Chris Tseng, Wafa Khamisy, Toan Vu Source \uff1a Computer Standards & Interfaces, Volume 28, Issue.\n \n \n \n \n "," \n \n \n \n \n \n Authors : Chun-Tang Chao, Chi-Jo Wang,\n \n \n \n \n "," \n \n \n \n \n \n The article written by Boyarshinova Vera Scientific adviser: Eltyshev Denis THE USE OF NEURO-FUZZY MODELS FOR INTEGRATED ASSESSMENT OF THE CONDITIONS OF.\n \n \n \n \n "," \n \n \n \n \n \n Dinner for Two. Fuzzify Inputs Apply Fuzzy Operator.\n \n \n \n \n "," \n \n \n \n \n \n VIDYA PRATISHTHAN\u2019S COLLEGE OF ENGINEERING, BARAMATI.\n \n \n \n \n "," \n \n \n \n \n \n Creating Neural Networks & FL Systems Objectives : By the end of this session Student will be able to: 1- Create ANN & FL Systems using toolboxes. 2-\n \n \n \n \n "," \n \n \n \n \n \n This time: Fuzzy Logic and Fuzzy Inference\n \n \n \n \n "," \n \n \n \n \n \n Single Tank System FV Desired liquid level: 5 cm (0.05 m)\n \n \n \n \n "," \n \n \n \n \n \n Artificial Intelligence CIS 342\n \n \n \n \n "," \n \n \n \n \n \n Visual Basic Code & No.: CS 218\n \n \n \n \n "," \n \n \n \n \n \n Fuzzy Logic Toolbox Analysis and Design.\n \n \n \n \n "," \n \n \n \n \n \n CANFIS Coactive Neuro Fuzzy Inference systems\n \n \n \n \n "," \n \n \n \n \n \n Fuzzy Inference Systems\n \n \n \n \n "," \n \n \n \n \n \n MATLAB Fuzzy Logic Toolbox\n \n \n \n \n "," \n \n \n \n \n \n TECHNOLOGY GUIDE FOUR Intelligent Systems.\n \n \n \n \n "," \n \n \n \n \n \n Introduction to Fuzzy Logic\n \n \n \n \n "," \n \n \n \n \n \n Artificial Intelligence and Adaptive Systems\n \n \n \n \n "," \n \n \n \n \n \n \u0645\u0646\u0637\u0642 \u0641\u0627\u0632\u06cc.\n \n \n \n \n "," \n \n \n \n \n \n Dr. Unnikrishnan P.C. Professor, EEE\n \n \n \n \n "," \n \n \n \n \n \n Dr. Unnikrishnan P.C. Professor, EEE\n \n \n \n \n "," \n \n \n \n \n \n INTELLIGENT CRUISE CONTROL WITH FUZZY LOGIC\n \n \n \n \n "," \n \n \n \n \n \n Social Media And Global Computing Introduction to Visual Studio\n \n \n \n \n "," \n \n \n \n \n \n Understanding the Visual IDE\n \n \n \n \n "," \n \n \n \n \n \n This time: Fuzzy Logic and Fuzzy Inference\n \n \n \n \n "," \n \n \n \n \n \n Plotting Data with MATLAB\n \n \n \n \n "," \n \n \n \n \n \n Digital Image Processing\n \n \n \n \n "," \n \n \n \n \n \n in Intelligent Tutoring Systems with Fuzzy Logic Techniques\n \n \n \n \n "," \n \n \n \n \n \n CSE 307 Basics of Image Processing\n \n \n \n \n "," \n \n \n \n \n \n Dr. Unnikrishnan P.C. Professor, EEE\n \n \n \n \n "," \n \n \n \n \n \n Chapter 15: GUI Applications & Event-Driven Programming\n \n \n \n \n "," \n \n \n \n \n \n This time: Fuzzy Logic and Fuzzy Inference\n \n \n \n \n "," \n \n \n \n \n \n Part of knowledge base of fuzzy logic expert system for exercise control of diabetics\n \n \n \n \n "," \n \n \n \n \n \n Dr. Unnikrishnan P.C. Professor, EEE\n \n \n \n \n "," \n \n \n \n \n \n Hybrid intelligent systems:\n \n \n \n \n "," \n \n \n \n \n \n Database Management System\n \n \n \n \n "," \n \n \n \n \n \n Fuzzy Inference Systems\n \n \n \n \n "," \n \n \n \n \n \n Expert Knowledge Based Systems\n \n \n \n \n "," \n \n \n \n \n \n Lecture 23 CS 507.\n \n \n \n \n "]; Similar presentations


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