scholarly journals Modeling Nonlinear Systems by a Fuzzy Logic Neural Network Using Genetic Algorithms

10.14311/296 ◽  
2001 ◽  
Vol 41 (6) ◽  
Author(s):  
Abdel-Fattah Attia ◽  
P. Horáček
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Membership Function ◽  
Fuzzy Set ◽  
Optimization Problems ◽  
Optimization Method ◽  
Fuzzy Approach ◽  
Gaussian Shape ◽  
Neuro Fuzzy ◽  
Definition Of

The main aim of this work is to optimize the parameters of the constrained membership function of the Fuzzy Logic Neural Network (FLNN). The constraints may be an indirect definition of the search ranges for every membership shape forming parameter based on 2nd order fuzzy set specifications. A particular method widely applicable in solving global optimization problems is introduced. This approach uses a Linear Adapted Genetic Algorithm (LAGA) to optimize the FLNN parameters. In this paper the derivation of a 2nd order fuzzy set is performed for a membership function of Gaussian shape, which is assumed for the neuro-fuzzy approach. The explanation of the optimization method is presented in detail on the basis of two examples.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

scholarly journals The conceptual model of neuro-fuzzy regulation of the microclimate in the room

2018 ◽  
Vol 251 ◽  
pp. 03020
Author(s):  
Andrey Karpenko ◽  
Irina Petrova
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Logic ◽  
Thermal Comfort ◽  
Conceptual Model ◽  
Fuzzy Logic Controller ◽  
Comfort Index ◽  
Neuro Fuzzy ◽  
Proposed Model ◽  
Artificial Neural

The purpose of this study is to develop a model of neuro-fuzzy regulation of the microclimate in the room. The proposed model consists of an artificial neural network serving to form a comfort index PMV, a fuzzy logic controller for regulating temperature and humidity in the room. This approach makes it easy to manage these parameters through an estimate of the PMV index, which indicates the level of thermal comfort in the room.

Logistics Demand Forecasting with Asymmetry-Width Probabilistic Fuzzy Logic System

2013 ◽  
Vol 706-708 ◽  
pp. 2012-2016
Author(s):  
Zhong Wei Wang ◽  
Li Xin Lu
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Sets ◽  
Membership Function ◽  
Fuzzy Set ◽  
Demand Forecasting ◽  
Fuzzy Logic System ◽  
The Asymmetry ◽  
Logistics Demand ◽  
Better Than ◽  
Logic System

There are a lot of approaches in logistics demand forecasting field and perform different characters. The probabilistic fuzzy set (PFS) and probabilistic fuzzy logic system is designed for handling the uncertainties in both stochastic and nonstochastic nature. In this paper, an asymmetric probabilistic fuzzy set is proposed by randomly varying the width of asymmetric Gaussian membership function. And the related PFLS is constructed to be applied to a logistics demand forecasting. The performance discloses that the asymmetry-width probabilistic fuzzy set performs better than precious symmetric one. It is because the asymmetric probabilistic fuzzy sets variability and malleability is higher than this of the symmetric probabilistic fuzzy set.

Neural Network Based Intelligent Learning of Fuzzy Logic Controller Parameters

2004 ◽  
Author(s):  
Manish Kumar ◽  
Devendra P. Garg
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Network Architecture ◽  
Fuzzy Logic Controller ◽  
Rule Base ◽  
Neural Network Architecture ◽  
The Neural Network ◽  
Neuro Fuzzy ◽  
Learning Capabilities ◽  
Optimal Signal

Design of an efficient fuzzy logic controller involves the optimization of parameters of fuzzy sets and proper choice of rule base. There are several techniques reported in recent literature that use neural network architecture and genetic algorithms to learn and optimize a fuzzy logic controller. This paper presents methodologies to learn and optimize fuzzy logic controller parameters that use learning capabilities of neural network. Concepts of model predictive control (MPC) have been used to obtain optimal signal to train the neural network via backpropagation. The strategies developed have been applied to control an inverted pendulum and results have been compared for two different fuzzy logic controllers developed with the help of neural networks. The first neural network emulates a PD controller, while the second controller is developed based on MPC. The proposed approach can be applied to learn fuzzy logic controller parameter online via the use of dynamic backpropagation. The results show that the Neuro-Fuzzy approaches were able to learn rule base and identify membership function parameters accurately.

scholarly journals Fuzzy Logic Optimization Method for Energy Efficiency Improvement of CFFS using GA in WSN

2020 ◽  
Vol 9 (6) ◽  
pp. 474-480
Keyword(s):  
Energy Efficiency ◽  
Fuzzy Logic ◽  
Membership Function ◽  
Optimization Method ◽  
Sensor Nodes ◽  
Function Parameter ◽  
Network Environment ◽  
Physical Damage ◽  
False Report ◽  
The Membership Function

Recently, the range of applications for wireless sensor networks has grown. In industrial applications using data-driven approaches, data reliability is particularly important. However, deployed sensor nodes can be easily damaged due to physical damage or node acquisition factors caused by attackers, and false report injection attacks may occur. CFFS with collaborative verification has been proposed to filter out false reports. The proposed CFFS reduces the probability of a successful attack by separating sensor nodes into clusters. The false report filtering performance in the existing scheme is determined according to the pre-security strength setting. Unfortunately, with CFFS, it is impossible to secure each cluster because multiple attacks in a region are not considered. DCFFS uses fuzzy logic to enable security management for each cluster in consideration of the network environment and the geographical arrangement of the nodes. It is necessary for a network administrator to adjust the scope of the membership function parameter to fit the network environment to ensure that the output has an appropriate security strength value for the environment; however, this is difficult to know because it has dissimilar optimum ranges for each application. This paper introduces a fuzzy optimization method that can be adapted to various environments using a genetic algorithm in CFFS. The energy efficiency of nodes is increased by correcting the scope of the membership function in the proposed method. We used experiments to verify that the energy efficiency of the proposed scheme is increased, as compared to the existing scheme.

scholarly journals Classical and Fuzzy Logic Evaluation of Students' Master Theses in Matlab Fuzzy Logic Toolbox Software: Dealing with Subjectivity in Human Reasoning

2018 ◽  
Vol 19 (2) ◽  
pp. 109
Author(s):  
Đurađ Hajder ◽  
Nikola Mićić
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Human Reasoning ◽  
Fuzzy Approach ◽  
Scientific Contribution ◽  
Mathematical Argumentation ◽  
Leading Role ◽  
Statistical Relevance ◽  
Definition Of ◽  
Levels Of Evaluation

The two−level evaluation of defined objectives, presented materials and methods and interpretation of results in master theses was done, in order to estimate their scientific contribution and statistical relevance. First level of evaluation was performed using classical methods and consisted of three steps: defining criteria of evaluation, analyzing their fulfilment and positioning 26 master theses into the Likert−type scale in the range from 0 to 1. Second level of evaluation was based on fuzzy logic methodology, conducted mostly in Matlab Fuzzy Logic Toolbox software and consisted of definition of variables, fuzzification, fuzzy inference, defuzzification and interpretation. Obtained marks from two levels of evaluation were than compared. Results indicate that fulfilment of defined criteria of evaluation is moderate. Common mistakes made by authors are accentuated, and clear advices for improving scientific contribution of theses were pointed out here. Classical evaluation marks were higher in 96.15% cases (or 25 out of 26 theses). However, fuzzy approach has advantages, which is also discussed. The interpretation of research results, defined as logical−mathematical argumentation, was found to have the leading role in forming mark in both levels of evaluation.

Computerized Electrocardiogram Diagnosis: Fuzzy Approach

1992 ◽  
Vol 31 (04) ◽  
pp. 225-233 ◽  
Author(s):  
Rosanna Degani
Keyword(s):  
Statistical Model ◽  
Fuzzy Set ◽  
Classification Problem ◽  
Diagnostic Process ◽  
Complex Task ◽  
Biological Variability ◽  
Fuzzy Approach ◽  
Deterministic Approach ◽  
Cardiac System ◽  
Definition Of

Abstract:This paper investigates the computerized analysis of electrocardiographic signals. The biological variability, the laáck of standards in the definition of measurements and of diagnostic criteria make the classification problem a complex task. Two basic methods of the diagnostic process are described: the statistical model and the deterministic approach. In particular, a model for ECG classification will be illustrated where the imprecise knowledge of the state of cardiac system and the vague definition of the pathological classes are taken care of by means of the fuzzy set formalism.

The Design of Ship Course Intelligent Controller Based on Adaptive Neural Fuzzy Interference System

2011 ◽  
Vol 135-136 ◽  
pp. 1037-1043
Author(s):  
Guan Shan Hu ◽  
Hai Rong Xiao
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
High Performance ◽  
Learning Algorithm ◽  
Fuzzy Interference System ◽  
Interference System ◽  
Ship Steering ◽  
Neuro Fuzzy ◽  
Online Learning Algorithm ◽  
Adaptive Control Algorithm

Under the condition that the nonlinearity of ship steering model is considered and the assumption that the parameters of the model are uncertain, we proposed an adaptive control algorithm for ship course nonlinear system by incorporating the technique of neural network and fuzzy logic system. In the paper, we presented the structure and characteristics of Adaptive Neuro-Fuzzy Interference System (ANFIS), established the ship course controller, and realized an online learning algorithm to do online parameter estimation. We utilize fuzzy logic to solve the uncertainty problem of control system, neural network to optimize the controller parameters. To demonstrate the applicability of the proposed method, simulation results are presented at the end of this paper. The experiment shows that the ANFIS controller can achieve high performance control under parameter perturbation and other disturbances.

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