scholarly journals Modeling and control of an unstable system using probabilistic fuzzy inference system

2015 ◽  
Vol 25 (3) ◽  
pp. 377-396
Author(s):  
N. Sozhamadevi ◽  
S. Sathiyamoorthy
Keyword(s):  
Fuzzy Sets ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Integrated Approach ◽  
Fuzzy Rules ◽  
Unstable System ◽  
Inference System ◽  
Modeling And Control ◽  
Highly Nonlinear ◽  
And Control

Abstract A new type Fuzzy Inference System is proposed, a Probabilistic Fuzzy Inference system which model and minimizes the effects of statistical uncertainties. The blend of two different concepts, degree of truth and probability of truth in a unique framework leads to this new concept. This combination is carried out both in Fuzzy sets and Fuzzy rules, which gives rise to Probabilistic Fuzzy Sets and Probabilistic Fuzzy Rules. Introducing these probabilistic elements, a distinctive probabilistic fuzzy inference system is developed and this involves fuzzification, inference and output processing. This integrated approach accounts for all of the uncertainty like rule uncertainties and measurement uncertainties present in the systems and has led to the design which performs optimally after training. In this paper a Probabilistic Fuzzy Inference System is applied for modeling and control of a highly nonlinear, unstable system and also proved its effectiveness.

scholarly journals Fuzzy Inference System Using Tsukamoto Method For Making Decision of Production (Case Study: PT Waru Kaltim Plantation)

METIK JURNAL ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 76-82
Author(s):  
Dominggus Norvindes Dellas ◽  
Ika Purnamasari ◽  
Nanda Arista Rizki
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Palm Oil ◽  
Oil Palm ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Fuzzy Rules ◽  
Decision Making Process ◽  
Inference System

The decision-making process using a fuzzy inference system (FIS) logic can use one of the methods called the Tsukamoto method. The process carried out in this method is the same as the fuzzy method in general, namely the formation of fuzzy sets, the fuzzification process, defuzzification, and measuring the accuracy of the result. The purpose of this study was to apply the Tsukamoto method to predict the yield of oil palm production at PT. Waru Kaltim Plantation. Based on the analysis using the Tsukamoto method, 36 fuzzy rules were obtained for each data from February 2013 to December 2015. The prediction results of palm oil production in 2013 did not change, except for May and August. In February, March, June, and August 2014 the level of production is constant, and almost throughout 2015, there was constant. The predicted MAPE for oil palm production was 31,522%, or in the fairly good category.

scholarly journals Prediction of dissolved oxygen in reservoirs using adaptive network-based fuzzy inference system

2011 ◽  
Vol 14 (1) ◽  
pp. 167-179 ◽  
Author(s):  
Vesna Ranković ◽  
Jasna Radulović ◽  
Ivana Radojević ◽  
Aleksandar Ostojić ◽  
Ljiljana Čomić
Keyword(s):  
Experimental Data ◽  
Water Quality ◽  
Dissolved Oxygen ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Adaptive Network ◽  
Inference System ◽  
Fuzzy Models ◽  
Highly Nonlinear ◽  
Input Variables

Predicting water quality is the key factor in the water quality management of reservoirs. Since a large number of factors affect the water quality, traditional data processing methods are no longer good enough for solving the problem. The dissolved oxygen (DO) level is a measure of the health of the aquatic system and its prediction is very important. DO dynamics are highly nonlinear and artificial intelligence techniques are capable of modelling this complex system. The objective of this study was to develop an adaptive network-based fuzzy inference system (ANFIS) to predict the DO in the Gruža Reservoir, Serbia. The fuzzy model was developed using experimental data which were collected during a 3-year period. The input variables analysed in this paper are: water pH, water temperature, total phosphate, nitrites, ammonia, iron, manganese and electrical conductivity. The selection of an appropriate set of input variables is based on the building of ANFIS models for each possible combination of input variables. Results of fuzzy models are compared with measured data on the basis of correlation coefficient, mean absolute error and mean square error. Comparing the predicted values by ANFIS with the experimental data indicates that fuzzy models provide accurate results.

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