scholarly journals Gaussian Membership Function used for Voice Recognition in Fuzzy Logic

2020 ◽  
Vol 8 (5) ◽  
pp. 2685-2689
Keyword(s):  
Fuzzy Logic ◽  
Membership Function ◽  
Group Membership ◽  
Voice Recognition ◽  
Age Group ◽  
Membership Functions ◽  
Human Voice ◽  
Degree Of Membership ◽  
Membership Value

Gaussian Membership function of a fuzzy set is a generalization form which is used to classify the human voice either based gender or age group. Membership functions were introduced by Zadeh in the first paper on fuzzy sets in the year 1965. In this paper we describe Gaussian membership function which we used to implement the simulation or classification of the human according to their age in fuzzy logic. A Gaussian Membership Function (MF) is a curve that defines how each point in the input space is mapped to a membership value (or degree of membership) between 0 and 1.

scholarly journals Learning Membership Functions in a Function-Based Object Recognition System

1995 ◽  
Vol 3 ◽  
pp. 187-222 ◽  
Author(s):  
K. Woods ◽  
D. Cook ◽  
L. Hall ◽  
K. Bowyer ◽  
L. Stark
Keyword(s):  
Object Recognition ◽  
Membership Function ◽  
Learning Algorithm ◽  
Physical Property ◽  
Recognition System ◽  
Tree Structure ◽  
Membership Functions ◽  
Recognition Systems ◽  
Membership Value ◽  
The Membership Function

Functionality-based recognition systems recognize objects at the category level by reasoning about how well the objects support the expected function. Such systems naturally associate a ``measure of goodness'' or ``membership value'' with a recognized object. This measure of goodness is the result of combining individual measures, or membership values, from potentially many primitive evaluations of different properties of the object's shape. A membership function is used to compute the membership value when evaluating a primitive of a particular physical property of an object. In previous versions of a recognition system known as Gruff, the membership function for each of the primitive evaluations was hand-crafted by the system designer. In this paper, we provide a learning component for the Gruff system, called Omlet, that automatically learns membership functions given a set of example objects labeled with their desired category measure. The learning algorithm is generally applicable to any problem in which low-level membership values are combined through an and-or tree structure to give a final overall membership value.

scholarly journals Fuzzy Optimization Model for post harvest selection process of Pecan (Carya illinoinensis)

2019 ◽  
Vol 8 (12S2) ◽  
pp. 421-425
Keyword(s):  
Fuzzy Logic ◽  
Selection Process ◽  
Fuzzy Optimization ◽  
Carya Illinoinensis ◽  
Membership Functions ◽  
Post Harvest ◽  
Quality Of Food ◽  
Selection Of

The quality of food is associated with a set of properties and characteristics that can be considered from its chemical and nutritional physical composition that have the ability to meet the needs of the consumer. The paper aims to evaluate the application of fuzzy logic in the evaluation and classification of the selection of pecans in the post harvest process using tests and instruments that determine their best quality. Fuzzy logic has proven to be very effective with matlab/simulink to develop and simulate the entire system, through an appropriate choice of rules and membership functions and applying the Mamdani method to defuzzification the results considering they are positive.

scholarly journals Classification of Eyes Based on Fuzzy Logic

2020 ◽  
Vol 14 (4) ◽  
pp. 101-112
Author(s):  
Mohamed Fakir ◽  
Hatimi Hicham ◽  
Mohamed Chabi ◽  
Muhammad Sarfraz
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Controller ◽  
Classification Problem ◽  
Second Step ◽  
Inference Rules ◽  
Effective Technique ◽  
Degree Of Membership ◽  
Characteristic Points ◽  
Representative Model

The systems of eye classification in an image are indispensable in several domains. To better find the class of membership of the eye in a minimal time, the classic methods of detection are inadequate. Fuzzy logic is considered to be an effective technique for solving an eye classification problem. This article proposes a fuzzy approach for eye classification. The tasks of classification are realized in two steps. In the first step, the characteristic points of the image are extracted in order to locate the eye. These characteristic points allow generating a representative model of the eye. In the second step, the detected eyes have to pass by a fuzzy controller containing several parts: Fuzzification, inference rules, and defuzzification. Finally, the system gives the degree of membership of the detected eyes to each class in the database.

Fuzzy Expert Framework for Diagnosis of Typhoid

2017 ◽  
Vol 2 (9) ◽  
pp. 13-16
Author(s):  
Okpokpong Nathaniel Ntebong Celestine ◽  
Umar Farouk Ibn Abdulrahman ◽  
Itoro Akpabio
Keyword(s):  
Fuzzy Logic ◽  
Abdominal Pain ◽  
Typhoid Fever ◽  
Membership Function ◽  
Enteric Fever ◽  
Salmonella Typhi ◽  
Medical Complications ◽  
Inference Rules ◽  
Membership Functions ◽  
Centroid Method

Typhoid fever is a disease that is caused by bacteria called salmonella typhi. It is also known as Enteric fever, Typhoid fever is been characterized by high fever, constipation, diarrhoea, abdominal pain, etc. It is often treatable when diagnosed early, but if left untreated could lead to other medical complications like intestinal haemorrhaging which may require major surgeries and could even lead to death. This paper proposes a method of diagnosis of Typhoid Fever using Fuzzy Logic. The system was built with twenty input membership functions, one output membership function and about two hundred inference rules which was simulated with MATLAB R2013 and therefore 97.5 % accuracy was obtained. The centroid method was used for the defuzzification. Although there are many systems in existence, this work is however based on the assumption that a system with a higher number of inference rules will make diagnosis a better.

scholarly journals SUM NORMAL OPTIMIZATION OF FUZZY MEMBERSHIP FUNCTIONS

2002 ◽  
Vol 10 (04) ◽  
pp. 363-384 ◽  
Author(s):  
DAN SIMON
Keyword(s):  
Fuzzy Logic ◽  
Membership Function ◽  
Optimization Problem ◽  
Fuzzy Membership ◽  
Optimization Approach ◽  
Membership Functions ◽  
Fuzzy Logic Systems ◽  
Fuzzy Membership Functions ◽  
Normal Constraint ◽  
Logic System

Given a fuzzy logic system, how can we determine the membership functions that will result in the best performance? If we constrain the membership functions to a certain shape (e.g., triangles or trapezoids) then each membership function can be parameterized by a small number of variables and the membership optimization problem can be reduced to a parameter optimization problem. This is the approach that is typically taken, but it results in membership functions that are not (in general) sum normal. That is, the resulting membership function values do not add up to one at each point in the domain. This optimization approach is modified in this paper so that the resulting membership functions are sum normal. Sum normality is desirable not only for its intuitive appeal but also for computational reasons in the real time implementation of fuzzy logic systems. The sum normal constraint is applied in this paper to both gradient descent optimization and Kalman filter optimization of fuzzy membership functions. The methods are illustrated on a fuzzy automotive cruise controller.

Optimization of Fuzzy Logic Controller for Trajectory Tracking Using Genetic Algorithm

2011 ◽  
Vol 15 (6) ◽  
pp. 639-651 ◽  
Author(s):  
Pintu Chandra Shill ◽  
◽  
M. A. H. Akhand ◽  
Md. Faijul Amin ◽  
Kazuyuki Murase ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Conventional Method ◽  
Membership Function ◽  
Expert Knowledge ◽  
Knowledge Bases ◽  
Membership Functions ◽  
Fuzzy Logic Controllers ◽  
Reversal Problem ◽  
Logic Controllers

Most Fuzzy Logic Controllers (FLCs) to date are working based on expert knowledge derived from heuristic knowledge of experienced operators. Conventional fuzzy logic controllers have poor adaptability due to invariable Membership Function (MF) parameters and fixed rule set. Conventional manual coded FLCs use only expert knowledge bases and do poorly with complex problems, especially with large numbers of input variables. We have developed FLCs using a Genetic Algorithm (GA) to automatically acquire knowledge that we call a genetic-fuzzy in which the GA is used to adaptively generate fuzzy rules and simultaneously selecting an appropriate MF shape. We also evaluate different membership functions in the fuzzy logic control. FLC sensibility is analysed and compared for different membership functions. We compare our proposed genetic-fuzzy approach to such existing methods, including as a manually coded conventional method, conventional method with complementary membership function, and a neuro-fuzzy method on a widely used test bed; backing up a truck reversal problem. Simulation results have shown our proposal to be superior to existing widely used methods.

Fuzzy Logic Based Voice Recognition As Per Their Gender And Age Group

2019 ◽  
Author(s):  
Shruti Agarwal ◽  
Ashish Agarwal ◽  
Prabhakar Gupta
Keyword(s):  
Fuzzy Logic ◽  
Voice Recognition ◽  
Age Group ◽  
Gender And Age

System Architecture and FPGA Embedding of Compact Fuzzy Logic Controller for Arm Robot Joints

2014 ◽  
Vol 493 ◽  
pp. 480-485 ◽  
Author(s):  
Bambang Siswoyo ◽  
M. Agus Choiron ◽  
Yudy Surya Irawan ◽  
I.N.G. Wardana
Keyword(s):  
Fuzzy Logic ◽  
Membership Function ◽  
System Architecture ◽  
Fuzzy Logic Controller ◽  
Robot Manipulator ◽  
Sampling Frequency ◽  
Membership Functions ◽  
Look Up Table ◽  
Servo Controller ◽  
Process Speed

This research is about the system architecture for embedding of the Compact Fuzzy Logic Controller (Compact-FLC) into the FPGA with a minimal need in device resource. This exciting research is to minimize the FPGA resources needed to build Compact-FLC based on FPGA for controlling each joint of arm robots manipulator. Compact-FLC results of this research have been used in the XILINX Spartan 3 XC3S1000 FPGA.The Compact-FLC has been applied with satisfactory results as Servo Controller for one joint of arm robot manipulator which the results showed that the controller achieved a process speed of 65,4uS, which is equivalent to a maximum sampling frequency of 15.290 KHz. Output membership function in this Compact-FLC used singleton membership function with Center Of Area algorithm. Two input membership functions, i.e E (Error) and CE (Change Error) have been used, both formed from several combination of triangular membership functions. The maximum number of fuzzysets that can be processed is sixteen. The overlapping function is not limited because there have been 256 if-then rule available as look up table in FPGA's ROM.The device utilization summary from ISE of XILINX development software gave the following data: Slice FlipFlops needed are 3869 or 25% of 15360 availability, 4 input LUT needed are 2319 or 15% of 15360 availability, Blocks of RAM needed are 4 or 16% of 24 availability, MULT18x18s needed are 2 or 8% of 24 availability, GCLKs needed are 2 or 25% of 8 availability, Bonded IOBs needed are 32 or 18% of 173 availability.

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