CHECKING ORTHOGONAL TRANSFORMATIONS AND GENETIC ALGORITHMS FOR SELECTION OF FUZZY RULES BASED ON INTERPRETABILITY-ACCURACY CONCEPTS

2012 ◽  
Vol 20 (supp02) ◽  
pp. 159-186 ◽  
Author(s):  
M. ISABEL REY ◽  
MARTA GALENDE ◽  
M. J. FUENTE ◽  
GREGORIO I. SAINZ-PALMERO
Keyword(s):  
Fuzzy System ◽  
Poor Performance ◽  
Fuzzy Modeling ◽  
Nsga Ii ◽  
Trade Off ◽  
Rule Selection ◽  
Fuzzy Models ◽  
Neuro Fuzzy ◽  
Firing Strength ◽  
Selection Of

Fuzzy modeling is one of the most known and used techniques in different areas to model the behavior of systems and processes. In most cases, as in data-driven fuzzy modeling, these fuzzy models reach a high performance from the point of view of accuracy, but from other points of view, such as complexity or interpretability, they can present a poor performance. Several approaches are found in the bibliography to reduce the complexity and improve the interpretability of the fuzzy models. In this paper, a post-processing approach is carried out via rule selection, whose aim is to choose the most relevant rules for working together on the well-known accuracy-interpretability trade-off. The rule relevancy is based on Orthogonal Transformations, such as the SVD-QR rank revealing approach, the P-QR and OLS transformations. Rule selection is carried out using a genetic algorithm that takes into account the information obtained by the Orthogonal Transformations. The main objective is to check the true significance, drawbacks and advantages of the rule selection based on the orthogonal transformations via the rule firing strength matrix. In order to carry out this aim, a neuro-fuzzy system, FasArt (Fuzzy Adaptive System ART based), and several case studies, data sets from the KEEL Project Repository, are used to tune and check this selection of rules based on orthogonal transformations, genetic selection and accuracy-interpretability trade-off. This neuro-fuzzy system generates Mamdani fuzzy rule based systems (FRBSs), in an approximative way. NSGA-II is the MOEA tool used to tune the proposed rule selection.

Neuro-Fuzzy System Modeling

2010 ◽  
pp. 147-175
Author(s):  
Chen-Sen Ouyang
Keyword(s):  
Neural Networks ◽  
Fuzzy System ◽  
Fuzzy Systems ◽  
System Modeling ◽  
Fuzzy Modeling ◽  
Structure Identification ◽  
Learning Capability ◽  
Computing Paradigm ◽  
Emerging Trends ◽  
Neuro Fuzzy

Neuro-fuzzy modeling is a computing paradigm of soft computing and very efficient for system modeling problems. It integrates two well-known modeling approaches of neural networks and fuzzy systems, and therefore possesses advantages of them, i.e., learning capability, robustness, human-like reasoning, and high understandability. Up to now, many approaches have been proposed for neuro-fuzzy modeling. However, it still exists many problems need to be solved. In this chapter, the authors firstly give an introduction to neuro-fuzzy system modeling. Secondly, some basic concepts of neural networks, fuzzy systems, and neuro-fuzzy systems are introduced. Also, they review and discuss some important literatures about neuro-fuzzy modeling. Thirdly, the issue for solving two most important problems of neuro-fuzzy modeling is considered, i.e., structure identification and parameter identification. Therefore, the authors present two approaches to solve these two problems, respectively. Fourthly, the future and emerging trends of neuro-fuzzy modeling is discussed. Besides, the possible research issues about neuro-fuzzy modeling are suggested. Finally, the authors give a conclusion.

scholarly journals Takagi-Sugeno Neuro-Fuzzy Modeling of a Multivariable Nonlinear Antenna System

2005 ◽  
Vol 2 (1) ◽  
pp. 12
Author(s):  
E. A. Al-Gallaf
Keyword(s):  
Dynamic System ◽  
Fuzzy System ◽  
System Modeling ◽  
Nonlinear Dynamic System ◽  
Fuzzy Modeling ◽  
Antenna System ◽  
Approximation Techniques ◽  
Intelligent Modeling ◽  
Neuro Fuzzy ◽  
Takagi Sugeno

This article investigates the use of a clustered based neuro-fuzzy system to nonlinear dynamic system modeling. It is focused on the modeling via Takagi-Sugeno (T-S) modeling procedure and the employment of fuzzy clustering to generate suitable initial membership functions. The T-S fuzzy modeling has been applied to model a nonlinear antenna dynamic system with two coupled inputs and outputs. Compared to other well-known approximation techniques such as artificial neural networks, the employed neuro-fuzzy system has provided a more transparent representation of the nonlinear antenna system under study, mainly due to the possible linguistic interpretation in the form of rules. Created initial memberships are then employed to construct suitable T-S models. Furthermore, the T-S fuzzy models have been validated and checked through the use of some standard model validation techniques (like the correlation functions). This intelligent modeling scheme is very useful once making complicated systems linguistically transparent in terms of the fuzzy if-then rules. 

scholarly journals Pareto Based Virtual Machine Selection with Load Balancing in Cloud Data Centre

2018 ◽  
Vol 18 (3) ◽  
pp. 23-36 ◽  
Author(s):  
Ketaki Bhalchandra Naik ◽  
G. Meera Gandhi ◽  
S. H. Patil
Keyword(s):  
Virtual Machine ◽  
Virtual Machines ◽  
Cloud Data Center ◽  
Nsga Ii ◽  
Trade Off ◽  
Cloud Data ◽  
Load Imbalance ◽  
Conflicting Objectives ◽  
Pareto Fronts ◽  
Selection Of

Abstract Cloud Data centers have adopted virtualization techniques for effective and efficient compilation of an application. The requirements of application from the execution perspective are fulfilled by scaling up and down the Virtual Machines (VMs). The appropriate selection of VMs to handle the unpredictable peak workload without load imbalance is a critical challenge for a cloud data center. In this article, we propose Pareto based Greedy-Non dominated Sorting Genetic Algorithm-II (G-NSGA2) for agile selection of a virtual machine. Our strategy generates Pareto optimal solutions for fair distribution of cloud workloads among the set of virtual machines. True Pareto fronts generate approximate optimal trade off solution for multiple conflicting objectives rather than aggregating all objectives to obtain single trade off solution. The objectives of our study are to minimize the response time, operational cost and energy consumption of the virtual machine. The simulation results evaluate that our hybrid NSGA-II outperforms as compared to the standard NSGA-II Multiobjective optimization problem.

scholarly journals Modeling of neuro-fuzzy system as a support in decision-making processes

2021 ◽  
Vol 2 (1) ◽  
pp. 222-234
Author(s):  
Darko Bozanic ◽  
◽  
Duško Tešić ◽  
Dragan Marinkovic ◽  
Aleksandar Milić ◽  
...  
Keyword(s):  
Decision Making ◽  
Fuzzy System ◽  
System Modeling ◽  
Multi Criteria Decision Making ◽  
Neuro Fuzzy ◽  
Decision Making Processes ◽  
Model Training ◽  
Input Variables ◽  
Selection Of ◽  
Decision Making Support

In the paper is presented Neuro-Fuzzy System as a decision-making support in the selection of construction machines (the example of selecting a loader is provided). Construction characteristics of a loader make the basis for selection, but also other elements of importance. The data for Neuro-Fuzzy System modeling are prepared using the Multi-Criteria Decision Making (MCDM) methods: Logarithm Methodology of Additive Weights (LMAW), VIKOR, TOPSIS, MOORA and SAW. The paper also presents the method of aggregation of weights of rules premises (AWRP), which defines the key rules of Neuro-Fuzzy System. Finally, the training of the model is tested. The data for the selection of input variables and for model training are obtained by engaging experts.

Development of a Neuro-Fuzzy System for Selection of Tree Species for Afforestation Purpose

2012 ◽  
pp. 317-330 ◽  
Author(s):  
Mrinmoy Majumder ◽  
Tilottama Chackraborty ◽  
Santanu Datta ◽  
Rajesh Chakraborty ◽  
Rabindra Nath Barman
Keyword(s):  
Tree Species ◽  
Fuzzy System ◽  
Neuro Fuzzy ◽  
Selection Of

Building Multi-Agent Systems With Reinforcement Hierarchical Neuro-Fuzzy Models

2016 ◽  
Author(s):  
Marcelo França Corrêa ◽  
Marley Vellasco ◽  
Karla Figueiredo
Keyword(s):  
Multi Agent Systems ◽  
Agent Systems ◽  
Fuzzy Models ◽  
Neuro Fuzzy ◽  
Multi Agent

Optimization of turn-milling process in roughing and finishing regimes: Trade-off between productivity, cutting force and surface integrity

2021 ◽  
pp. 095440892199851
Author(s):  
Ebrahim Hosseini ◽  
Shafiqur Rehman ◽  
Ashkan Alimoradi
Keyword(s):  
Cutting Force ◽  
Desirability Function ◽  
Milling Process ◽  
Machining Process ◽  
Hybrid Machining ◽  
Trade Off ◽  
Hybrid Machining Process ◽  
Process Factors ◽  
Turn Milling ◽  
Selection Of

Turn-milling is a hybrid machining process which used benefits of interrupted cutting for proceeding of round bars. However, number of controllable parameters in the hybrid process is numerous that makes optimizing the process complicated. In the present study, an optimization work has been proposed to investigate the trade-off between production rate and cutting force in roughing regime as well surface roughness and tensile residual stress in finishing regime. Number of 43 experiments based on response surface methodology was designed and carried out to gather required data for development of quadratic empirical models. Then, the adequacy and importance of process factors were analyzed using analysis of variances. Finally, desirability function was used to optimize the process in rough and finish machining regimes. The obtained results showed that selection of eccentricity and cutter speed at their maximum working range can effectively enhance the quality characteristics in both the roughing and finishing regimes.

Sign in / Sign up

Export Citation Format

Share Document