Dynamic fuzzy systems modeling

2016 ◽  
pp. 41-60
Keyword(s):  
Fuzzy Systems ◽  
Systems Modeling

Fuzzy Systems Modeling

2011 ◽  
pp. 734-743
Author(s):  
Young Hoon Joo ◽  
Guanrong Chen
Keyword(s):  
Fuzzy Systems ◽  
System Modeling ◽  
Systems Modeling ◽  
Empirical Knowledge ◽  
Input Output ◽  
Output Data ◽  
Basic Objective ◽  
Unknown System

The basic objective of system modeling is to establish an input-output representative mapping that can satisfactorily describe the system behaviors, by using the available input-output data based upon physical or empirical knowledge about the structure of the unknown system.

Dynamic fuzzy systems modeling

2019 ◽  
pp. 185-203
Author(s):  
Adedeji B. Badiru
Keyword(s):  
Fuzzy Systems ◽  
Systems Modeling

Fuzzy systems modeling in practice

2001 ◽  
Vol 121 (1) ◽  
pp. 73-93 ◽  
Author(s):  
P.J. Costa Branco ◽  
J.A. Dente
Keyword(s):  
Fuzzy Systems ◽  
Systems Modeling

Daily river flow prediction based on Two-Phase Constructive Fuzzy Systems Modeling: A case of hydrological – meteorological measurements asymmetry

2018 ◽  
Vol 558 ◽  
pp. 255-265 ◽  
Author(s):  
Bassam Bou-Fakhreddine ◽  
Imad Mougharbel ◽  
Alain Faye ◽  
Sara Abou Chakra ◽  
Yann Pollet
Keyword(s):  
Fuzzy Systems ◽  
River Flow ◽  
Systems Modeling ◽  
Two Phase ◽  
Flow Prediction

A Novel Approach to Construct Fuzzy Systems Based on Fireworks Optimization Algorithm

2016 ◽  
Vol 25 (2) ◽  
pp. 185-195
Author(s):  
Xiaodong Zhu ◽  
Chong Liu ◽  
Yamo Guo
Keyword(s):  
Optimization Algorithm ◽  
Fuzzy Systems ◽  
Systems Modeling ◽  
High Effectiveness ◽  
Fitness Functions ◽  
Fuzzy Models ◽  
Optimal Balance ◽  
Novel Approach ◽  
Non Linear System ◽  
Swarm Intelligence Algorithm

AbstractTo find an optimal balance between accuracy and model transparency in fuzzy systems modeling, a novel approach based on the non-dominated sorting fireworks optimization algorithm (NSFOA) is proposed in this paper. The FOA is a new swarm intelligence algorithm mimicking the explosion of fireworks, which could be used to tune the parameters and optimize the structure of fuzzy systems with high effectiveness and correctness. At each iteration of the NSFOA algorithm, all individuals are sorted based on fast non-dominated sorting and crowding-distance evaluation, and the multiobjective fitness functions consider the aspects of both interpretability and accuracy contemporaneously. Subsequently, the individuals with better transparency and accuracy are selected for the next generation. The proposed NSFOA algorithm is applied to the second-order non-linear system and the Iris benchmark problem, and the experimental results demonstrate that our method leads to comprehensible fuzzy models with remarkable accuracy.

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