scholarly journals APPLICATION OF MAMDANI FUZZY METHOD TO DETERMINE ROUND BREAD PRODUCTION AT PT VANESSA BAKERY

2019 ◽  
Vol 8 (3) ◽  
pp. 204
Author(s):  
A. A. I. DWI FIBRIAYORA ◽  
G.K. GANDHIADI ◽  
NI KETUT TARI TASTRAWATI ◽  
I PUTU EKA NILA KENCANA
Keyword(s):  
Fuzzy System ◽  
Fuzzy Rules ◽  
Total Production ◽  
Membership Functions ◽  
Fuzzy Method ◽  
Bread Production

Mamdani Fuzzy is a method that interprets input values and makes conclusions based on IF-THEN rules and producing the output. In this research Mamdani fuzzy method is applied to determine the amount of round bread production at PT Vanessa Bakery. The step involve:  determining the fuzzy system, the membership functions, as well as the fuzzy rules. The defuzzification process is applied to determine the amount of total production and to calculate the MAPE value of the Mamdani fuzzy method. The calculated MAPE as much as 5.94%, indicates this method has an excellent forecasting ability because the value is less than 10%. Thus, the Mamdani fuzzy method can be used at PT Vanessa Bakery.

Rough Set-Based Neuro-Fuzzy System

2011 ◽  
pp. 1396-1403
Author(s):  
Kai Keng Ang ◽  
Chai Quek
Keyword(s):  
Rough Set ◽  
Fuzzy System ◽  
Fuzzy Model ◽  
Fuzzy Rules ◽  
Membership Functions ◽  
Linguistic Terms ◽  
Neural Learning ◽  
Neuro Fuzzy ◽  
Fuzzy Neural ◽  
Modeling Data

Neuro-fuzzy hybridization is the oldest and most popular methodology in soft computing (Mitra & Hayashi, 2000). Neuro-fuzzy hybridization is known as Fuzzy Neural Networks, or Neuro-Fuzzy Systems (NFS) in the literature (Lin & Lee, 1996; Mitra & Hayashi, 2000). NFS is capable of abstracting a fuzzy model from given numerical examples using neural learning techniques to formulate accurate predictions on unseen samples. The fuzzy model incorporates the human-like style of fuzzy reasoning through a linguistic model that comprises of if-then fuzzy rules and linguistic terms described by membership functions. Hence, the main strength of NFS in modeling data is universal approximation (Tikk, Kóczy, & Gedeon, 2003) with the ability to solicit interpretable if-then fuzzy rules (Guillaume, 2001). However, modeling data using NFS involves the contradictory requirements of interpretability versus accuracy. Prevailingly, NFS that focused on accuracy employed optimization which resulted in membership functions that derailed from human-interpretable linguistic terms, or employed large number of if-then fuzzy rules on high-dimensional data that exceeded human level interpretation. This article presents a novel hybrid intelligent Rough set-based Neuro-Fuzzy System (RNFS). RNFS synergizes the sound concept of knowledge reduction from rough set theory with NFS. RNFS reinforces the strength of NFS by employing rough set-based techniques to perform attribute and rule reductions, thereby improving the interpretability without compromising the accuracy of the abstracted fuzzy model.

AUTOMATIC DEVELOPMENT OF FUZZY MEMBERSHIP FUNCTIONS ON HEPATITIS PATIENTS DATA USING PARTICLE SWARM OPTIMIZATION (PSO)

2015 ◽  
Vol 77 (22) ◽  
Author(s):  
Candra Dewi ◽  
Ratna Putri P.S ◽  
Indriati Indriati
Keyword(s):  
Particle Swarm Optimization ◽  
Membership Function ◽  
Fuzzy System ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Membership Functions ◽  
Swarm Optimization ◽  
Global Weight ◽  
The Status ◽  
And Function

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.  

Achieving the best defuzzifier in terminology of Persian sentences through classified fuzzy method

2020 ◽  
Vol 39 (3) ◽  
pp. 2921-2934
Author(s):  
Pardis Pirayesh ◽  
Homayun Motameni ◽  
Ebrahim Akbari
Keyword(s):  
Fuzzy Logic ◽  
Success Rate ◽  
Fuzzy System ◽  
Fuzzy Method ◽  
Infinite Space ◽  
Persian Language

Fuzzy logic is a multi-valued concept, whose emergence in software sciences has eliminated 0 and 1 computations, putting them within an infinite space of [0,1]. This characteristic of fuzzy logic has resolved ambiguity in numerous previous problems. The sentence roles in Persian language were specified based on the fuzzy logic’s capability to resolve ambiguity. For that purpose, we first obtained the best classification for each defuzzifier, based on which a classified fuzzy was implemented. Nonetheless, the fuzzy system used in this research was classified based on statistical computations. To achieve the best classification, five defuzzification methods (Mean Of Max, Max Of Membership, Largest Of Max, Smallest Of Max, and Central Average) competed in 16 roles each in five classes (different matrices). Finally, Mean of Max with a success rate of 64% proved to be a defuzzifier delivering the best output among 5 different defuzzification methods.

scholarly journals An Improved and Adaptive Approach in ANFIS to Predict Knee Diseases

2020 ◽  
Vol 15 (2) ◽  
pp. 22-37 ◽  
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.

Identifying Risk Factors of Diabetes using Fuzzy Inference System

2017 ◽  
Vol 6 (4) ◽  
pp. 150
Author(s):  
Lazim Abdullah
Keyword(s):  
Risk Factors ◽  
Blood Pressure ◽  
Serum Cholesterol ◽  
Fuzzy Rules ◽  
Diabetes Research ◽  
Membership Functions ◽  
Contributing Factors ◽  
Output Variable ◽  
Inference System ◽  
Level Of Risk

Identification of the real risk factors of diabetes is still very much inconclusive. In this paper, fuzzy rules based system was devised to identify risk factors of diabetes. The system consists of five input variables: Body Mass Index, age, blood pressure, Creatinine, and serum cholesterol and one output variable: level of risk. Three Gaussian membership functions for linguistic terms are defined for each input variable. The level of risk is defined using three triangular membership functions to represent output variable. Based on the information from patients’ clinical audit reports, the system was used to classify the level of risk of fifty patients that currently undergoing regular diagnosis for diabetes treatment. The system successfully classified the risk into three levels of Low, Medium and High where three main contributing factors toward developing diabetes were identified.<br /> The three risk factors are age, blood pressure and serum cholesterol.<br /> The multi-input system that characterised by IF-THEN fuzzy rules provide easily interpretable result for identifying predictors of diabetes. Research to establish reproducibility and validity of the findings are left for future works.

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