scholarly journals Design Of Fuzzy Rule-Based System Using Genetic Algorithms

2021 ◽  
Vol 23 (11) ◽  
pp. 683-692
Author(s):  
Sanjay Charaya ◽  
◽  
Kapil Mehta ◽  
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Fuzzy Rule ◽  
Fixed Number ◽  
Fuzzy Rules ◽  
Observation Data ◽  
Dimensional Solution ◽  
Air Conditioning System ◽  
Rule Based ◽  
Genetic Algorithm Approach

The aim of this paper is to obtain a compact and optimal fuzzy rule-based model from observation data by utilizing the Genetic algorithm technique. The approach is optimized by applying Genetic Algorithms, owing to its capability of searching irregular and high dimensional solution spaces. Genetic Algorithms has been applied to learn consequent part of fuzzy rules and models with fixed number of rules. In the work we propose a Genetic algorithm approach to a non-linear air conditioning system for the construction of optimal fuzzy rules in two steps. First, fuzzy clustering is applied to obtain an initial rule based model having pre-calculated number of rules with antecedents only. In the second step, the regions of rule-consequents are obtained by a binary coded Genetic Algorithm which leads to the extraction of an optimal rule based model.

Corner Detection Using Fuzzy Principles

2013 ◽  
pp. 498-512
Author(s):  
Erik Cuevas ◽  
Daniel Zaldivar ◽  
Marco Perez-Cisneros
Keyword(s):  
Pattern Recognition ◽  
Fuzzy Systems ◽  
Fuzzy Rule ◽  
Important Task ◽  
Fuzzy Rules ◽  
Corner Detection ◽  
Illumination Change ◽  
Benchmark Test ◽  
Rule Based ◽  
Rule Based System

Reliable corner detection is an important task in pattern recognition applications. In this chapter an approach based on fuzzy-rules to detect corners even under imprecise information is presented. The uncertainties arising due to various types of imaging defects such as blurring, illumination change, noise, et cetera. Fuzzy systems are well known for efficient handling of impreciseness. In order to handle the incompleteness arising due to imperfection of data, it is reasonable to model corner properties by a fuzzy rule-based system. The robustness of the proposed algorithm is compared with well known conventional detectors. The performance is tested on a number of benchmark test images to illustrate the efficiency of the algorithm in noise presence.

Combining Machine Learning and Fuzzy Rule-Based System in Automating Signal Timing Experts’ Decisions during Non-Recurrent Congestion

2020 ◽  
Vol 2674 (6) ◽  
pp. 163-176
Author(s):  
Mosammat Tahnin Tariq ◽  
Aidin Massahi ◽  
Rajib Saha ◽  
Mohammed Hadi
Keyword(s):  
Machine Learning ◽  
Recursive Partitioning ◽  
Fuzzy Rule ◽  
Traffic Signal ◽  
Fuzzy Rules ◽  
Signal Timing ◽  
Rule Based ◽  
Rule Based System ◽  
Machine Learning Model ◽  
History Of

Events such as surges in demand or lane blockages can create queue spillbacks even during off-peak periods, resulting in delays and spillbacks to upstream intersections. To address this issue, some transportation agencies have started implementing processes to change signal timings in real time based on traffic signal engineers’ observations of incident and traffic conditions at the intersections upstream and downstream of the congested locations. Decisions to change the signal timing are governed by many factors, such as queue length, conditions of the main and side streets, potential of traffic spilling back to upstream intersections, the importance of upstream cross streets, and the potential of the queue backing up to a freeway ramp. This paper investigates and assesses automating the process of updating the signal timing plans during non-recurrent conditions by capturing the history of the responses of the traffic signal engineers to non-recurrent conditions and utilizing this experience to train a machine learning model. A combination of recursive partitioning and regression decision tree (RPART) and fuzzy rule-based system (FRBS) is utilized in this study to deal with the vagueness and uncertainty of human decisions. Comparing the decisions made based on the resulting fuzzy rules from applying the methodology with previously recorded expert decisions for a project case study indicates accurate recommendations for shifts in the green phases of traffic signals. The simulation results indicate that changing the green times based on the output of the fuzzy rules decreased delays caused by lane blockages or demand surge.

scholarly journals A Fuzzy Classifier with Feature Selection Based on the Gravitational Search Algorithm

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 609 ◽  
Author(s):  
Marina Bardamova ◽  
Anton Konev ◽  
Ilya Hodashinsky ◽  
Alexander Shelupanov
Keyword(s):  
Feature Selection ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Fuzzy Rule ◽  
Search Space ◽  
Fuzzy Rules ◽  
Rule Base ◽  
Rule Based ◽  
Gravitational Search ◽  
Rule Based Classifier

This paper concerns several important topics of the Symmetry journal, namely, pattern recognition, computer-aided design, diversity and similarity. We also take advantage of the symmetric and asymmetric structure of a transfer function, which is responsible to map a continuous search space to a binary search space. A new method for design of a fuzzy-rule-based classifier using metaheuristics called Gravitational Search Algorithm (GSA) is discussed. The paper identifies three basic stages of the classifier construction: feature selection, creating of a fuzzy rule base and optimization of the antecedent parameters of rules. At the first stage, several feature subsets are obtained by using the wrapper scheme on the basis of the binary GSA. Creating fuzzy rules is a serious challenge in designing the fuzzy-rule-based classifier in the presence of high-dimensional data. The classifier structure is formed by the rule base generation algorithm by using minimum and maximum feature values. The optimal fuzzy-rule-based parameters are extracted from the training data using the continuous GSA. The classifier performance is tested on real-world KEEL (Knowledge Extraction based on Evolutionary Learning) datasets. The results demonstrate that highly accurate classifiers could be constructed with relatively few fuzzy rules and features.

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