Classification of Multi-Class Datasets Using 2D Membership Functions in TSK Fuzzy System

Information about the status of disease (prognosis) for patients with hepatitis is important to determine the type of action to stabilize and cure this disease. Among some system, fuzzy system is one of the methods that can be used to obtain this prognosis. In the fuzzification process, the determination of the exact range of membership function will influence the calculation of membership degree and of course will affect the final value of fuzzy system. This range and function can usually be formed using intuition or by using an algorithm. In this paper, Particle Swarm Optimization (PSO) algorithm is implemented to form the triangular membership functions in the case of patients with hepatitis. For testing process, this paper conducts four scenarios to find the best combination of PSO parameter values . Based on the testing it was found that the best parameters to form a membership function range for the hepatitis data is about 0.9, 0.1, 2, 2, 100, 500 for inertia max, inertia min, local ballast constant, global weight constant, the number of particles, and maximum iterations respectively.

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Classification Of Wheat Grains Using Image Processing Techniques Based Neuro-Fuzzy System Model

Journal of Intelligent Systems with Applications ◽

10.54856/jiswa.201805018 ◽

2018 ◽

pp. 57-61

Author(s):

Ahmet Kayabasi ◽

Kadir Sabanci ◽

Abdurrahim Toktas

Keyword(s):

Image Processing ◽

Fuzzy System ◽

Absolute Error ◽

Data Set ◽

Wheat Grains ◽

Image Processing Techniques ◽

Neuro Fuzzy ◽

Length Width ◽

Processing Techniques

In this study, an image processing techniques (IPTs) and a Sugeno-typed neuro-fuzzy system (NFS) model is presented for classifying the wheat grains into bread and durum. Images of 200 wheat grains are taken by a high resolution camera in order to generate the data set for training and testing processes of the NFS model. The features of 5 dimensions which are length, width, area, perimeter and fullness are acquired through using IPT. Then NFS model input with the dimension parameters are trained through 180 wheat grain data and their accuracies are tested via 20 data. The proposed NFS model numerically calculate the outputs with mean absolute error (MAE) of 0.0312 and classify the grains with accuracy of 100% for the testing process. These results show that the IPT based NFS model can be successfully applied to classification of wheat grains.

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MISO Takagi-Sugeno Fuzzy System with Linear Membership Functions

Analytical Methods in Fuzzy Modeling and Control - Studies in Fuzziness and Soft Computing ◽

10.1007/978-3-540-89927-3_2 ◽

2009 ◽

pp. 3-24

Author(s):

Jasek Kluska

Keyword(s):

Fuzzy System ◽

Membership Functions ◽

Takagi Sugeno

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Fuzzy system for classification of microarray data using a hybrid ant stem optimisation algorithm

International Journal of Advanced Intelligence Paradigms ◽

10.1504/ijaip.2021.10035156 ◽

2021 ◽

Vol 18 (2) ◽

pp. 154

Author(s):

Pugalendhi GaneshKumar ◽

S. Arul Antran Vijay

Keyword(s):

Microarray Data ◽

Fuzzy System ◽

Optimisation Algorithm

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An Improved and Adaptive Approach in ANFIS to Predict Knee Diseases

International Journal of Healthcare Information Systems and Informatics ◽

10.4018/ijhisi.2020040102 ◽

2020 ◽

Vol 15 (2) ◽

pp. 22-37 ◽

Cited By ~ 1

Author(s):

Ranjit Kaur ◽

Kamaldeep Kaur ◽

Aditya Khamparia ◽

Divya Anand

Keyword(s):

Fuzzy System ◽

Inflammatory Diseases ◽

Medical Science ◽

Research Work ◽

Membership Functions ◽

Anfis Model ◽

Comparative Performance ◽

Medical Practitioners ◽

Neuro Fuzzy ◽

Testing Accuracy

Artificial intelligence is emerging as a persuasive tool in the field of medical science. This research work also primarily focuses on the development of a tool to automate the diagnosis of inflammatory diseases of the knee joint. The tool will also assist the physicians and medical practitioners for diagnosis. The diseases considered for this research under inflammatory category are osteoarthritis, rheumatoid arthritis and osteonecrosis. A five-layer adaptive neuro-fuzzy (ANFIS) architecture was used to model the system. The ANFIS system works by mapping input parameters to the input membership functions, input membership functions are mapped to the rules generated by the ANFIS model which are further mapped to the output membership function. A comparative performance analysis of fuzzy system and ANFIS system is also done and results generated shows that the ANFIS system outperformed fuzzy system in terms of testing accuracy, sensitivity and specificity.

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Speech Recognition Using Combined Fuzzy and Ant Colony algorithm

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v6i5.10105 ◽

2016 ◽

Vol 6 (5) ◽

pp. 2205

Author(s):

Fooad Jalili ◽

Milad Jafari Barani

Keyword(s):

Speech Recognition ◽

Dimensionality Reduction ◽

Input Signal ◽

Speech Signal ◽

Fuzzy System ◽

Ant Colony Algorithm ◽

Noise Removal ◽

Ant Colony ◽

Speech Detection

<p><span>In recent years various methods has been proposed for speech recognition and removing noise from the speech signal became an important issue. In this paper a fuzzy system has been proposed for speech recognition that can obtain accurate results using classification of speech signals with “Ant Colony” algorithm. First, speech samples are given to the fuzzy system to obtain a pattern for every set of signals that can be helpful for dimensionality reduction, easier checking of outcome and better recognition of signals. Then, the “ACO” algorithm is used to cluster these signals and determine a cluster for each input signal. Also, with this method we will be able to recognize noise and consider it in a separate cluster and remove it from the input signal. Results show that the accuracy for speech detection and noise removal is desirable.</span></p>

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