Exploration of Fuzzy System With Applications

2021 ◽  
pp. 550-569
Author(s):  
Shivlal Mewada ◽  
Pradeep Sharma ◽  
S. S. Gautam
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Genetic Algorithms ◽  
Fuzzy System ◽  
Fuzzy Systems ◽  
Feed Forward Neural Network ◽  
Inference Procedure ◽  
System User ◽  
Fuzzy Automata ◽  
Technological Area

Fuzzy system was altered from a ‘buzz word' to an important technological area, with various publications in international conferences and transactions. Several Japanese products applying fuzzy logic concepts, such as household appliances and electronic equipment, power engineering, robotics, and optimization have been manufactured. This system is capable to process and learn mathematical data as well as linguistic data. Fuzzy system user linguistic explanations for the variables and linguistic procedures for the I/P-O/P behavior. In this chapter, present the application of fuzzy system with data mining, neural networks, fuzzy automata, and genetic algorithms. It also presents the foundation of fuzzy data Mining, with the fuzzification inference procedure and defuzzification procedure, fuzzy systems and neural networks with feed forward neural network, FNN with it features generalization of Fuzzy Automata, and sixth fuzzy systems and genetic algorithms. The chapter explores a popular fuzzy system model to show complex systems and an application of fuzzy system.

Exploration of Fuzzy System With Applications

2018 ◽  
pp. 479-498 ◽  
Author(s):  
Shivlal Mewada ◽  
Pradeep Sharma ◽  
S. S. Gautam
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Genetic Algorithms ◽  
Fuzzy System ◽  
Fuzzy Systems ◽  
Feed Forward Neural Network ◽  
Inference Procedure ◽  
System User ◽  
Fuzzy Automata ◽  
Technological Area

Fuzzy system was altered from a ‘buzz word' to an important technological area, with various publications in international conferences and transactions. Several Japanese products applying fuzzy logic concepts, such as household appliances and electronic equipment, power engineering, robotics, and optimization have been manufactured. This system is capable to process and learn mathematical data as well as linguistic data. Fuzzy system user linguistic explanations for the variables and linguistic procedures for the I/P-O/P behavior. In this chapter, present the application of fuzzy system with data mining, neural networks, fuzzy automata, and genetic algorithms. It also presents the foundation of fuzzy data Mining, with the fuzzification inference procedure and defuzzification procedure, fuzzy systems and neural networks with feed forward neural network, FNN with it features generalization of Fuzzy Automata, and sixth fuzzy systems and genetic algorithms. The chapter explores a popular fuzzy system model to show complex systems and an application of fuzzy system.

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 Special Issue on Intelligent Technologies, Fuzzy Systems and Applications

2003 ◽  
Vol 7 (3) ◽  
pp. 251-251
Author(s):  
Nguyen Hoang Phuong ◽  
Keyword(s):  
Data Mining ◽  
Genetic Algorithms ◽  
Soft Computing ◽  
Fuzzy Systems ◽  
Wide Spectrum ◽  
Support Vector ◽  
Equivalent Transformation ◽  
Self Organizing Map ◽  
Special Issue

In this issue, we are featuring fifteen papers devoted to intelligent technologies, fuzzy systems and their applications as a special issue of the journal. The papers are selected from papers accepted and presented at the joint Third International Conference on Intelligent Technologies and Third Vietnam-Japan Symposium on Fuzzy Systems and Applications (InTech/VJFuzzy'2002) that was held in Hanoi, Vietnam on December 3-5, 2002. In InTech/VJFuzzy'2002, there was a wide spectrum of research topics on artificial intelligence, fuzzy systems, soft computing, and related fields such as"fuzzy logic", "fuzzy set theory", "fuzzy systems", "AI techniques", "Bayesian networks", "genetic algorithms", "neural networks", "knowledge discovery and data mining", "speech recognition", "soft computing in medicine", among others. More than 60 papers were accepted and presented by authors from many countries, including Australia, China, India, Korea, Germany, France, Thailand, Taiwan, Japan, Vietnam, and U.S.A. Fifteen papers that received outstanding recommendations from its reviews were selected in this special issue. The topics addressed by these selected papers include fuzzy rule systems, fuzzy inference methods, fuzzy and rough models, problem solving with equivalent transformation, genetic algorithms, reinforcement learning, non–monotonic reasoning, support vector machines, Hedge algebra, intelligent control, natural language understanding, self–organizing map learning, soft computing and data mining in medicine. As editors of this special issue, we would like to express our sincere gratitude to paper's authors in this issue. Our special thanks go to the anonymous referees for their excellent job, Ms. Kumiko Sato for her help in coordinating the publication of the issue, the Editorial Board of JACIII, especially Professor Kaoru Hirota for his great support and encouragement. Finally, we wish to thank Professors Hung T. Nguyen, Michio Sugeno and Pratit Santiprabhob for their help and contribution to InTech/VJFuzzy'2002. Without their support, the InTech/VJFuzzy'2002 and this issue would not be possible.

Data Mining in Public Administration

2008 ◽  
pp. 556-567
Author(s):  
John Wang ◽  
Xiaohua Hu ◽  
Dan Zhu
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Cluster Analysis ◽  
Genetic Algorithms ◽  
Inductive Learning ◽  
Analytical Technique ◽  
Analytical Skills ◽  
Information Construction ◽  
Knowledge Rules ◽  
General Data

Data mining involves searching through databases for potentially useful information such as knowledge rules, patterns, regularities, and other trends hidden in the data. In order to complete these tasks, the contemporary data mining packages offer techniques such as neural networks, inductive learning decision trees, cluster analysis, link analysis, genetic algorithms, visualization, and so forth (Hand, Mannila, & Smyth, 2001; Wang, 2006). In general, data mining is a data analytical technique that assists businesses in learning and understanding their customers so that decisions and strategies can be implemented most accurately and effectively to maximize profitability. Data mining is not general data analysis, but a comprehensive technique that requires analytical skills, information construction, and professional knowledge.

Adaptive Neuro-Fuzzy Systems

2011 ◽  
pp. 31-36
Author(s):  
Larbi Esmahi ◽  
Kristian Williamson ◽  
Elarbi Badidi
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Artificial Neural Networks ◽  
Soft Computing ◽  
Fuzzy Systems ◽  
Neuro Fuzzy ◽  
Artificial Neural ◽  
Tools And Techniques ◽  
Traditional Approaches

Fuzzy logic became the core of a different approach to computing. Whereas traditional approaches to computing were precise, or hard edged, fuzzy logic allowed for the possibility of a less precise or softer approach (Klir et al., 1995, pp. 212-242). An approach where precision is not paramount is not only closer to the way humans thought, but may be in fact easier to create as well (Jin, 2000). Thus was born the field of soft computing (Zadeh, 1994). Other techniques were added to this field, such as Artificial Neural Networks (ANN), and genetic algorithms, both modeled on biological systems. Soon it was realized that these tools could be combined, and by mixing them together, they could cover their respective weaknesses while at the same time generate something that is greater than its parts, or in short, creating synergy. Adaptive Neuro-fuzzy is perhaps the most prominent of these admixtures of soft computing technologies (Mitra et al., 2000). The technique was first created when artificial neural networks were modified to work with fuzzy logic, hence the Neuro-fuzzy name (Jang et al., 1997, pp. 1-7). This combination provides fuzzy systems with adaptability and the ability to learn. It was later shown that adaptive fuzzy systems could be created with other soft computing techniques, such as genetic algorithms (Yen et al., 1998, pp. 469-490), Rough sets (Pal et al., 2003; Jensen et al., 2004, Ang et al., 2005) and Bayesian networks (Muller et al., 1995), but the Neuro-fuzzy name was widely used, so it stayed. In this chapter we are using the most widely used terminology in the field. Neuro-fuzzy is a blanket description of a wide variety of tools and techniques used to combine any aspect of fuzzy logic with any aspect of artificial neural networks. For the most part, these combinations are just extensions of one technology or the other. For example, neural networks usually take binary inputs, but use weights that vary in value from 0 to 1. Adding fuzzy sets to ANN to convert a range of input values into values that can be used as weights is considered a Neuro-fuzzy solution. This chapter will pay particular interest to the sub-field where the fuzzy logic rules are modified by the adaptive aspect of the system. The next part of this chapter will be organized as follows: in section 1 we examine models and techniques used to combine fuzzy logic and neural networks together to create Neuro-fuzzy systems. Section 2 provides an overview of the main steps involved in the development of adaptive Neuro-fuzzy systems. Section 3 concludes this chapter with some recommendations and future developments.

scholarly journals Adaptive Probabilistic Neuro-Fuzzy System and its Hybrid Learning in Medical Diagnostics Task

2021 ◽  
Vol 14 (1) ◽  
pp. 123-129
Author(s):  
Yevgeniy Bodyanskiy ◽  
Anastasiia Deineko ◽  
Iryna Pliss ◽  
Olha Chala
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fuzzy System ◽  
Fuzzy Systems ◽  
Hybrid Learning ◽  
Medical Diagnostics ◽  
Speed Of Information Processing ◽  
Neuro Fuzzy ◽  
Artificial Neural ◽  
Overlapping Classes

Background: The medical diagnostic task in conditions of the limited dataset and overlapping classes is considered. Such limitations happen quite often in real-world tasks. The lack of long training datasets during solving real tasks in the problem of medical diagnostics causes not being able to use the mathematical apparatus of deep learning. Additionally, considering other factors, such as in a dataset, classes can be overlapped in the feature space; also data can be specified in various scales: in the numerical interval, numerical ratios, ordinal (rank), nominal and binary, which does not allow the use of known neural networks. In order to overcome arising restrictions and problems, a hybrid neuro-fuzzy system based on a probabilistic neural network and adaptive neuro-fuzzy interference system that allows solving the task in these situations is proposed. Methods: Computational intelligence, artificial neural networks, neuro-fuzzy systems compared to conventional artificial neural networks, the proposed system requires significantly less training time, and in comparison with neuro-fuzzy systems, it contains significantly fewer membership functions in the fuzzification layer. The hybrid learning algorithm for the system under consideration based on self-learning according to the principle “Winner takes all” and lazy learning according to the principle “Neurons at data points” has been introduced. Results: The proposed system solves the problem of classification in conditions of overlapping classes with the calculation of the membership levels of the formed diagnosis to various possible classes. Conclusion: The proposed system is quite simple in its numerical implementation, characterized by a high speed of information processing, both in the learning process and in the decision-making process; it easily adapts to situations when the number of diagnostics features changes during the system's functioning.

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