Selection of input variables of fuzzy model using genetic algorithm with quick fuzzy inference

1997 ◽  
pp. 45-53 ◽  
Author(s):  
S. Matsushita ◽  
T. Furuhashi ◽  
H. Tsutsui ◽  
Y. Uchikawa
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Input Variables ◽  
Selection Of

Selection of Input Variables for the Prediction of Wind Speed in Wind Farms Based on Genetic Algorithm

2011 ◽  
Vol 35 (6) ◽  
pp. 649-660 ◽  
Author(s):  
R. A. Gupta ◽  
Rajesh Kumar ◽  
Ajay Kumar Bansal
Keyword(s):  
Genetic Algorithm ◽  
Wind Speed ◽  
Wind Farms ◽  
Input Variables ◽  
Selection Of

Structure Organization of Hierarchical Fuzzy Model Using Genetic Algorithm

1995 ◽  
Vol 7 (1) ◽  
pp. 29-35
Author(s):  
Toshio Fukuda ◽  
◽  
Yasuhisa Hasegawa ◽  
Koji Shimojima
Keyword(s):  
Genetic Algorithm ◽  
Hierarchical Structure ◽  
Back Propagation ◽  
Fuzzy Model ◽  
Numerical Data ◽  
Fuzzy Rules ◽  
Input And Output ◽  
The Hierarchical Structure ◽  
Input Variables ◽  
The Relationship

This paper proposes a method to organize the hierarchical structure of fuzzy model using the Genetic Algorithm and back-propagation method. The number of fuzzy rules increases exponentially with the number of input variables. Thus, a fuzzy system with many input variables has an extremely large number of fuzzy rules. Hierarchical structure of fuzzy reasoning is one of the methods to reduce the number of fuzzy rules and membership functions. However, it is very difficult to organize the hierarchical structure because the hierarchical structure cannot be constructed without considering the relationship among input and output variables. The proposed method can organize the suitable hierarchical structure for the relationship among input and output variables in teaching numerical data. It is based on the Genetic Algorithm with an evaluation function as a strategy that adopts a system with fewer fuzzy rules and more accurate outputs. The proposed method is applied to the approximation problems of multi-dimensional nonlinear functions in order to demonstrate its effectiveness.

scholarly journals Classification of Bidikmisi Scholarship Acceptance using Neural Network Based on Hybrid Method of Genetic Algorithm

2021 ◽  
Vol 5 (2) ◽  
pp. 396-404
Author(s):  
N Cahyani ◽  
Sinta Septi Pangastuti ◽  
K Fithriasari ◽  
Irhamah Irhamah ◽  
N Iriawan
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Optimal Number ◽  
Connection Weight ◽  
The Neural Network ◽  
Network Method ◽  
Input Variables ◽  
Human Brains ◽  
Selection Of

A Neural network is a series of algorithms that endeavours to recognize underlying relationships in a set of data through processes that mimic the way human brains operate. In the case of classification, this method can provide a fit model through various factors, such as the variety of the optimal number of hidden nodes, the variety of relevant input variables, and the selection of optimal connection weights. One popular method to achieve the optimal selection of connection weights is using a Genetic Algorithm (GA), the basic concept is to iterate over Darwin's evolution. This research presents the Neural Network method with the Backpropagation Neural Network (BPNN) and the combined method of BPNN with GA, where GA is used to initialize and optimize the connection weight of BPNN. Based on accuracy value, the BPNN method combined with GA provides better classification, which is 90.51%, in the case of Bidikmisi Scholarship classification in East Java.

scholarly journals An Application of Fuzzy Sets to the Blastability Index (BI) Used in Rock Engineering

2018 ◽  
Author(s):  
Aref Alipour ◽  
Mojtaba Mokharian ◽  
Sajjad Chehreghani
Keyword(s):  
Experimental Data ◽  
Rock Mass ◽  
Fuzzy Sets ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Empirical Models ◽  
Conventional Model ◽  
Inference System ◽  
Input Variables ◽  
Blastability Index

Rock masses have inherently different resistance to fragmentation by blasting. This property is hereafter referred to as the blastability of a rock mass. Empirical models for the estimation of blastability have been developed. In this study, the Mamdani fuzzy algorithm was used to express the blastability index by fuzzy sets. We use Lilly and Ghose blastability models which are important models of blastability. Parameters of these models were represented by fuzzy sets as the input variables of the fuzzy model. The output of the fuzzy model is a final blastability index rating. Experimental data is obtained from seven mine and one dam sites in Iran. BI values are obtained from both BI fuzzy inference system and conventional BI; Fuzzy sets have more adjustment than conventional model.

scholarly journals Application of fuzzy modeling in the evaluation of crisis state of enterprises

2019 ◽  
pp. 66-71
Author(s):  
M. N. Belousova ◽  
A. A. Dashkov
Keyword(s):  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Rule Base ◽  
Software Environment ◽  
Inference Procedure ◽  
Relative Level ◽  
Inference System ◽  
Innovation Potential ◽  
Input Variables ◽  
Fuzzy Assessment

The features of the proposed fuzzy model for assessing the crisis state of enterprises have been disclosed. The MATLAB software environment has been selected as the environment for building a fuzzy output system. In the model of a fuzzy assessment of the crisis state of enterprises, the following input linguistic variables have been highlighted: the relative level of financial status, the probability of bankruptcy, the level of information security, the level of innovation potential. The terms of the input variables and the result variable have been described. The rule base for fuzzy inference system has been formulated. The results of modeling the assessment of the crisis state of enterprises have been represented by a fuzzy inference procedure.

scholarly journals A fuzzy model for the evaluation of suppliers of material resources to machine-building enterprises

2016 ◽  
pp. 245-277
Author(s):  
Evgeniya Kozlova ◽  
Vladimir Volynsky
Keyword(s):  
Supplier Selection ◽  
Fuzzy Inference ◽  
Selection Process ◽  
Fuzzy Model ◽  
Estimation Algorithm ◽  
Machine Building ◽  
Material Resources ◽  
Engineering Enterprise ◽  
Selection Of ◽  
Building Enterprises

The subject of our research is the process of selecting a supplier of material resources to machine-building enterprises. The aim of this work is to identify factors that improve the process of supplier selection of material resources to large enterprises. Testing this model is made on the example of the “KRANEKS” machinebuilding enterprise. The methodological basis of the scientific research is work in the fields of systems analysis, management accounting, and econometrics. As a result of the research, a methodology and estimation algorithm of suppliers was developed, as well as a hierarchical model of fuzzy inference to supplier evaluation. These developments are mainly aimed at supporting decision making in choosing the best provider for a machine-building enterprise. These developments mainly focused on decision support in choosing the best provider for an engineering enterprise whose list of material resources runs into the thousands. They are aimed at improving the efficiency of administrative decisions in the supplier selection process of machine-building enterprises and improving the work process of their purchasing departments.

scholarly journals Fuzzy Logic Systems for Diagnosis of Renal Cancer

2020 ◽  
Vol 10 (10) ◽  
pp. 3464
Author(s):  
Nikita Jindal ◽  
Jimmy Singla ◽  
Balwinder Kaur ◽  
Harsh Sadawarti ◽  
Deepak Prashar ◽  
...  
Keyword(s):  
Renal Cancer ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Diagnostic System ◽  
Fuzzy Logic Systems ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Medical Diagnostic ◽  
Input Variables ◽  
Medical Diagnostic System

Renal cancer is a serious and common type of cancer affecting old ages. The growth of such type of cancer can be stopped by detecting it before it reaches advanced or end-stage. Hence, renal cancer must be identified and diagnosed in the initial stages. In this research paper, an intelligent medical diagnostic system to diagnose renal cancer is developed by using fuzzy and neuro-fuzzy techniques. Essentially, for a fuzzy inference system, two layers are used. The first layer gives the output about whether the patient is having renal cancer or not. Similarly, the second layer detects the current stage of suffering patients. While in the development of a medical diagnostic system by using a neuro-fuzzy technique, the Gaussian membership functions are used for all the input variables considered for the diagnosis. In this paper, the comparison between the performance of developed systems has been done by taking some suitable parameters. The results obtained from this comparison study show that the intelligent medical system developed by using a neuro-fuzzy model gives the more precise and accurate results than existing systems.

Input variables selection of fuzzy dynamic models by using genetic algorithm

2016 ◽  
Author(s):  
Juan Manuel Escano ◽  
Adolfo J. Sanchez ◽  
Kritchai Witheephanich ◽  
Samira Roshany-Yamchi
Keyword(s):  
Genetic Algorithm ◽  
Dynamic Models ◽  
Variables Selection ◽  
Input Variables ◽  
Selection Of
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