Assessing water quality in rivers with fuzzy inference systems: A case study

2006 ◽  
Vol 32 (6) ◽  
pp. 733-742 ◽  
Author(s):  
William Ocampo-Duque ◽  
Núria Ferré-Huguet ◽  
José L. Domingo ◽  
Marta Schuhmacher
Keyword(s):  
Water Quality ◽  
Fuzzy Inference ◽  
Fuzzy Inference Systems ◽  
Inference Systems

Wind power conversion system model identification using adaptive neuro-fuzzy inference systems: A case study

Energy ◽  
2021 ◽  
pp. 122089
Author(s):  
Boudy Bilal ◽  
Kondo Hloindo Adjallah ◽  
Alexandre Sava ◽  
Kaan Yetilmezsoy ◽  
Emel Kıyan
Keyword(s):  
Wind Power ◽  
Fuzzy Inference ◽  
Power Conversion ◽  
Model Identification ◽  
System Model ◽  
Fuzzy Inference Systems ◽  
Neuro Fuzzy ◽  
Conversion System ◽  
Inference Systems

scholarly journals Hybrid modelling of biological systems using fuzzy continuous Petri nets

2019 ◽  
Author(s):  
Fei Liu ◽  
Wujie Sun ◽  
Monika Heiner ◽  
David Gilbert
Keyword(s):  
Petri Nets ◽  
Kinetic Data ◽  
Fuzzy Inference ◽  
Biological Systems ◽  
Integrated Modelling ◽  
Fuzzy Inference Systems ◽  
Hybrid Modelling ◽  
Continuous Petri Nets ◽  
Inference Systems

Abstract Integrated modelling of biological systems is challenged by composing components with sufficient kinetic data and components with insufficient kinetic data or components built only using experts’ experience and knowledge. Fuzzy continuous Petri nets (FCPNs) combine continuous Petri nets with fuzzy inference systems, and thus offer an hybrid uncertain/certain approach to integrated modelling of such biological systems with uncertainties. In this paper, we give a formal definition and a corresponding simulation algorithm of FCPNs, and briefly introduce the FCPN tool that we have developed for implementing FCPNs. We then present a methodology and workflow utilizing FCPNs to achieve hybrid (uncertain/certain) modelling of biological systems illustrated with a case study of the Mercaptopurine metabolic pathway. We hope this research will promote the wider application of FCPNs and address the uncertain/certain integrated modelling challenge in the systems biology area.

scholarly journals Comparison performance of Artificial Neural Networks and Fuzzy Inference systems in forecasting precious metals price Case Study: Gold, Silver, Platinum and Palladium

2020 ◽  
Vol 3 (1) ◽  
pp. 749-762
Author(s):  
Abdulsaboor Mahmoodzada ◽  
Suhrab Ahadi ◽  
Abdul Basir Mahmoodzada
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fuzzy Inference ◽  
Precious Metals ◽  
Global Market ◽  
Fuzzy Inference Systems ◽  
Gold Silver ◽  
Artificial Neural ◽  
Inference Systems

Awareness about the price of precious metals and the correct prediction on the process of taking decision can bring facilities, and purchasing them in the global market and recognizing the specific time of dealing can cause investment. In this article comparison of the performance of Artificial Neural Networks and Fuzzy Inference Systems in predicting the price of the precious metals (Case Study: Gold, Silver, Platinum and Palladium).has been pointed. The information about each of these metals (Gold, Silver, Platinum and Palladium) is monthly considered from 1998 until 2018 including 360 data. Thus, by examining different influential variables, National Product Parameters, Time, getting higher the value of USD dollar against the Canadian dollar, global production and global reserves of precious metals are chosen as influential variables. In this research, implementation of (ANFIS) is made for the prediction model by using Artificial and Fuzzy Neural Model. Evaluation of models by using coefficient values, the average set of squares and the square root of the average set of the squares in order of the values for Gold 0.9964 , 0.000268 & 0.01637 for silver 0.987, 0.000092 & 0.0096, for platinum 0.9976, 0.000209 & 0.01448 and for palladium 0.99, 0.0001 & 0.01 have been achieved. As a result, while the best predictive model for the price of gold and platinum is Artificial Neural Networks, the model of ANFIS is suggested for the silver and palladium.

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