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Uncertain spatial reasoning of environmental risks in GIS using genetic learning algorithms

Rouzbeh Shad, Arefeh Shad
2011 Environmental Monitoring & Assessment  
Use of genetic fuzzy capabilities enables the algorithm to learn and be tuned to proper rules in a flexible manner and increases the preciseness and robustness of operations.  ...  Modeling the impact of air pollution is one of the most important approaches for managing damages to the ecosystem.  ...  They can learn and tune different components of the fuzzy rule-based system by optimizing the parameters (Adoption) and generating rules (Learning) in a genetic fuzzy framework.  ... 
doi:10.1007/s10661-011-2421-z pmid:22068317 fatcat:lrjqdzqfw5cobe6kkmsw3zpwii

CREATION OF HYBRID INTELLIGENT SYSTEM FOR NONLINEAR RELATIONS IDENTIFICATION

Mukhamedieva D. T, Safarova L.U
2017 International Journal of Research in Engineering and Technology  
In view of this, the methods of nonlinear relation identification based on hybrid intelligent system formation are examined in the current article.  ...  Information on parameters of such processes is expressed by experts in words and statements, i.e. in linguistic form.  ...  A compound part of ЕС -genetic algorithms are the algorithms of global optimization based on mechanisms of natural selection and genetics [8] .  ... 
doi:10.15623/ijret.2017.0609005 fatcat:j7lusinurrgjbaiicbphnvohmi

Fuzzy Rule Based Inference System for Implementation of Naval Military Mission

Rashmi Singh, Vipin Saxena
2018 International Journal of Computer Network and Information Security  
The present system expects to help the choice about changing a unit to a mission considering that ambiguity and unpredictability of information by methods of fuzzy concepts and imitates the selection procedure  ...  of a human trained by means of a rule-based inference system.  ...  analyze six proposals for this general method and also presented a method to learn the various parameters of this fuzzy reasoning method [41] .  ... 
doi:10.5815/ijcnis.2018.04.04 fatcat:rtzgd6oduzfv3d35ctagen2iwa

Application of Artificial Intelligence Techniques in Reactive Power/Voltage Control of Power System

Gehao Sheng, Guangyu Tu, Yi Luo
2014 Electronics Science Technology and Application  
In this paper the main results and methods of applying the AI techniques, such as Expert System (ES), Artificial Neural Network (ANN), Fuzzy Theory (FT), Genetic Algorithm (GA) and Multi-Agent System (  ...  and a lot of results in this field are obtained.  ...  Fuzzy control is a practical method of controlling simulate human fuzzy reasoning and decisionmaking process and its control according to the known rules and data, is derived from the fuzzy input output  ... 
doi:10.18686/esta.v1i1.2 fatcat:gfy3xbcgz5fkbpfasjueyqieyq

Enabling external factors for consumption electricity forecasting using hybrid genetic algorithm and fuzzy neural system

Gayatri Dwi Santika
2017 2017 4th International Conference on Computer Applications and Information Processing Technology (CAIPT)  
By using two phase on Fuzzy Inference system and Genetic algorithm for optimization, weight can improve the accuracy of electricity load forecasting.  ...  The relationship external factors like temperature, humidity, price load, Gross Domestic Product and load is identified with a case study for a particular region.  ...  For the neural network method, the weaknesses depend on a number of hidden layer, neurons, and the epoch is very noteworthy because its a strong impact on the forecast that will be produced.  ... 
doi:10.1109/caipt.2017.8320708 fatcat:5ishfekuynenlo4lncvqti7hvy

Fuzzy Evolutionary Algorithms and Genetic Fuzzy Systems: A Positive Collaboration between Evolutionary Algorithms and Fuzzy Systems [chapter]

F. Herrera, M. Lozano
2009 Intelligent Systems Reference Library  
The first one involves the application of evolutionary algorithms for solving optimization and search problems related with fuzzy systems, obtaining genetic fuzzy systems.  ...  In this chapter, we shortly introduce genetic fuzzy systems and fuzzy evolutionary algorithms, giving a short state of the art, and sketch our vision of some hot current trends and prospects.  ...  When considering a rule based system and focusing on learning rules, the different genetic learning methods follow two approaches in order to encode rules within a population of individuals: • The "Chromosome  ... 
doi:10.1007/978-3-642-01799-5_4 fatcat:7pwjyyl3b5delpvesat3wmvqom

Genetic Rules Induction Fuzzy Inference System for Classification and Regression Application in Energy Industry

2019 International Journal of Engineering and Advanced Technology  
This paper presents development of a new rules induction algorithm namely genetic rules induction fuzzy inference system for classification and regression (GRIFISCnR) that combines genetic algorithm with  ...  It divulges the advantage of optimization with ease of understanding for classification and regression of energy performance of buildings, transformer, and harmonic current in energy industry.  ...  PROPOSED METHOD AND ALGORITHM A typical genetic KB learning involves genetic rules learning, selection, DB learning and simultaneous learning of KB components [13] [14] [27] .  ... 
doi:10.35940/ijeat.b4923.129219 fatcat:2bw4o7omkza43bqyl4pyxhhylm

Genetic fuzzy systems: taxonomy, current research trends and prospects

Francisco Herrera
2008 Evolutionary Intelligence  
The use of genetic algorithms for designing fuzzy systems provides them with the learning and adaptation capabilities and is called genetic fuzzy systems (GFSs).  ...  This paper gives an overview of the field of GFSs, being organized in the following four parts: (a) a taxonomy proposal focused on the fuzzy system components involved in the genetic learning process;  ...  of a reasoning method; and a defuzzification interface, that translates the fuzzy rule action thus obtained to a real action using a defuzzification method.  ... 
doi:10.1007/s12065-007-0001-5 fatcat:m6rflzvbzverren3icl4oz4n6a

A Survey on various Machine Learning Approaches for ECG Analysis

C. K., B. S.
2017 International Journal of Computer Applications  
The existing methods are compared and contrasted based on qualitative and qualitative parameters viz., purpose of the work, algorithms adopted and results obtained.  ...  Electrocardiogram (ECG) is a P, QRS and T wave demonstrating the electrical activity of the heart.  ...  For this purposes, Behadada & Chikh, (2013) conducted a study on assessing the cardiac abnormalities by optimizing the classification of cardiac arrhythmias and detection of abnormalities.  ... 
doi:10.5120/ijca2017913737 fatcat:conppaqjgnb3rgqsqwjffweq44

Multiobjective Genetic Optimization of Fuzzy Partitions and T-Norm Parameters in Fuzzy Classifiers

Edward Hinojosa Cardenas, Heloisa A. Carmago
2012 2012 Brazilian Symposium on Neural Networks  
This paper proposes the use of a multiobjective genetic algorithm to tune fuzzy partitions and t-norm parameters in Fuzzy Rule Based Classifications Systems (FRBCSs).  ...  We present a comparative study which examines a number of t-norms and their influence in the quality of the non-dominated solutions found in the optimization process.  ...  In this paper, we focus on the optimization of the tnorms that influence the fuzzy reasoning method of a FRBCS, as described in section II.  ... 
doi:10.1109/sbrn.2012.45 dblp:conf/sbrn/CardenasC12 fatcat:xvvdwrw4b5hihozpbdkv7g574a

A Multi-Agent System to Assist with Property Valuation Using Heterogeneous Ensembles of Fuzzy Models [chapter]

Magdalena Graczyk, Tadeusz Lasota, Zbigniew Telec, Bogdan Trawiński
2010 Lecture Notes in Computer Science  
The major part of the study was devoted to investigate the predictive accuracy of heterogeneous ensembles comprising fuzzy models and to compare them with homogenous bagging ensembles.  ...  Six optimization heuristics including genetic, tabu search, simulated annealing, minimum average and random algorithms were implemented and applied to obtain the best ensembles for different number of  ...  N N519 407437 funded by Polish Ministry of Science and Higher Education (2009-2012).  ... 
doi:10.1007/978-3-642-13480-7_44 fatcat:cdftygzhpvbytlh5vtxzyd5xhu

Interval-valued fuzzy cognitive maps with genetic learning for predicting corporate financial distress

Petr Hajek, Ondrej Prochazka
2018 Filomat  
In traditional FCMs, optimization algorithms are used to learn the strengths of the relationships from the data. Here, we propose a novel IVFCM with real-coded genetic learning.  ...  Specifically, we show that this method outperforms FCMs, fuzzy grey cognitive maps and adaptive neuro-fuzzy systems in terms of root mean squared error.  ...  Therefore, this study can serve as a base for future research on IVFCM learning.  ... 
doi:10.2298/fil1805657h fatcat:t4ruc2zrpnahhopqciqs7co3k4

Models-based Optimization Methods for the Specication of Fuzzy Inference Systems in Discrete EVent Simulation

Paul-Antoine Bisgambiglia, Bastien Poggi, Celine Nicolai
2011 Proceedings of the 7th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-2011)  
Fuzzy Inference Systems (FIS) have the advantage of relying on the properties of Fuzzy Logic to represent imperfect information so gradually, and manipulate them from a linguistic description.  ...  We focus mainly on the used new aproach about using genetic algoritm in order to optimize the FIS.  ...  This new method is intended to extend the application elds of the DEVS formalism in order to make learning, optimization and control.They operate from fuzzy reasoning rules, which have the advantage of  ... 
doi:10.2991/eusflat.2011.6 dblp:conf/eusflat/BisgambigliaPN11 fatcat:txv3f4bw7jgxbabllg6h3xxwxe

Intelligent System for Efficiency Enhancing Program of Thermal Power Plant: A Case Study
english

S. PANDA, P.S. RATHA, DR. N.K. BAR PANDA
2014 International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering  
Prior to implementation, it is advisable to perform simulation studies on a plant model.  ...  Thermal power plant being large and complex, the design of its control system involves numerous problems, such as costs, quality, environmental impact, safety, reliability, accuracy, and robustness.  ...  It is based on fuzzy logic, artificial neural networks and probabilistic reasoning including genetic algorithms.  ... 
doi:10.15662/ijareeie.2014.0310007 fatcat:wvfx63twfnb5ncbnrrzxgnqzcq

Fuzzy Inference System Optimization by Evolutionary Approach for Mobile Robot Navigation

Fatma Boufera, Fatima Debbat, Nicolas Monmarché, Mohamed Slimane, Mohamed Faycal Khelfi
2018 International Journal of Intelligent Systems and Applications  
In order to generate optimal parameters of fuzzy controller, this work propose a learning and optimization process based on ant colony algorithm ACO and genetic algorithm operators (crossover and mutation  ...  The simulated results show clearly the impact of the optimization approach improves the fuzzy controller performance mainly in obstacle avoidance and detection of the shortest path.  ...  For our case study, we opted for the first approach to learning the optimization fuzzy rules necessary for navigation of the mobile robot.  ... 
doi:10.5815/ijisa.2018.02.08 fatcat:ouhf6jji25fujimuyzkof3d3x4
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