A Fuzzy Graph Recurrent Neural Network Approach for the Prediction of Radial Overcut in Electro Discharge Machining
01 Jan 2021-pp 257-270
TL;DR: F fuzzy graph recurrent neural network architecture is used for modelling and predicting the radial over cut for an electro discharge machining information system.
Abstract: Manufacturing of goods rely on its design methodology and the process parameters. The parameters used in manufacturing process play an important role to build a quality product. Initially heuristic techniques are used for parameter selection. Many researchers conducted research to predict the radial overcut using neural networks. Besides, fuzzy neural network gains more popularity due to presence of fuzzy system and neural network. In this paper fuzzy graph recurrent neural network architecture is used for modelling and predicting the radial over cut for an electro discharge machining information system.
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18 Sep 2000
TL;DR: Simulation results on five difficult test problems show that the proposed NSGA-II, in most problems, is able to find much better spread of solutions and better convergence near the true Pareto-optimal front compared to PAES and SPEA--two other elitist multi-objective EAs which pay special attention towards creating a diverse Paretimal front.
Abstract: Multi-objective evolutionary algorithms which use non-dominated sorting and sharing have been mainly criticized for their (i) O(MN3) computational complexity (where M is the number of objectives and N is the population size), (ii) non-elitism approach, and (iii) the need for specifying a sharing parameter. In this paper, we suggest a non-dominated sorting based multi-objective evolutionary algorithm (we called it the Non-dominated Sorting GA-II or NSGA-II) which alleviates all the above three difficulties. Specifically, a fast non-dominated sorting approach with O(MN2) computational complexity is presented. Second, a selection operator is presented which creates a mating pool by combining the parent and child populations and selecting the best (with respect to fitness and spread) N solutions. Simulation results on five difficult test problems show that the proposed NSGA-II, in most problems, is able to find much better spread of solutions and better convergence near the true Pareto-optimal front compared to PAES and SPEA--two other elitist multi-objective EAs which pay special attention towards creating a diverse Pareto-optimal front. Because of NSGA-II's low computational requirements, elitist approach, and parameter-less sharing approach, NSGA-II should find increasing applications in the years to come.
4,878 citations
TL;DR: By providing a basis for a systematic approach to approximate reasoning, the theory of fuzzy sets may well have a substantial impact on scientific methodology in the years ahead, particularly in the realms of psychology, economics, law, medicine, decision analysis, information retrieval, and artificial intelligence.
Abstract: The papers presented in this volume were contributed by participants in
the U.S.-Japan Seminar on Fuzzy Sets and Their Applications, held at the
University of California, Berkeley, in July 1974. These papers cover a broad
spectrum of topics related to the theory of fuzzy sets, ranging from its mathematical aspects to applications in human cognition, communication, decisionmaking, and engineering systems analysis. Basically, a fuzzy set is a class in which there may be a continuum of grades of membership as, say, in the class of long objects. Such sets underlie much of our ability to summarize, communicate, and make decisions under uncertainty or partial information. Indeed, fuzzy sets appear to play an essential role in human cognition, especially in relation to concept formation, pattern classification, and logical reasoning.
Since its inception about a decade ago, the theory of fuzzy sets has evolved in many directions, and is finding pplications in a wide variety of fields in which the phenomena under study are too complex or too ill defined to be analyzed by conventional techniques. Thus, by providing a basis for a systematic approach to approximate reasoning, the theory of fuzzy sets may well have a substantial impact on scientific methodology in the years ahead, particularly in the realms of psychology, economics, law, medicine, decision analysis, information retrieval, and artificial intelligence.
The U.S.-Japan Seminar on Fuzzy Sets was sponsored by the U.S.-Japan Cooperative Science Program, with the joint support of the National Science Foundation and the Japan Society for the Promotion of Science. In organizing the seminar, the co-chairmen received considerable help from J.E. O’Connell and L. Trent of the National Science Foundation; the staff of the Japan Society for the Promotion of Science; and D. J. Angelakos and his staff at the University of California, Berkeley. As co-editors of this volume, we wish also to express our heartfelt appreciation to Terry Brown for her invaluable assistance in the preparation of the manuscript, and to Academic Press for undertaking its publication.
For the convenience of the reader, a brief introduction to the theory of fuzzy sets is provided in the Appendix of the first paper in this volume. An up-to-date bibliography on fuzzy sets and their applications is included at the
end of the volume.
903 citations
TL;DR: A novel method of object recognition from an imprecise multiobserver data has been presented here and involves construction of a Comparison Table from a fuzzy soft set in a parametric sense for decision making.
822 citations
Book•
11 Mar 2008
TL;DR: Fuzzy sets are a class in which there may be a continuum of grades of membership as, say, in the class of long objects as mentioned in this paper, which underlie much of our ability to summarize, communicate, and make decisions under uncertainty or partial information.
Abstract: The papers presented in this volume were contributed by participants in
the US-Japan Seminar on Fuzzy Sets and Their Applications, held at the
University of California, Berkeley, in July 1974 These papers cover a broad
spectrum of topics related to the theory of fuzzy sets, ranging from its mathematical aspects to applications in human cognition, communication, decisionmaking, and engineering systems analysis Basically, a fuzzy set is a class in which there may be a continuum of grades of membership as, say, in the class of long objects Such sets underlie much of our ability to summarize, communicate, and make decisions under uncertainty or partial information Indeed, fuzzy sets appear to play an essential role in human cognition, especially in relation to concept formation, pattern classification, and logical reasoning
Since its inception about a decade ago, the theory of fuzzy sets has evolved in many directions, and is finding pplications in a wide variety of fields in which the phenomena under study are too complex or too ill defined to be analyzed by conventional techniques Thus, by providing a basis for a systematic approach to approximate reasoning, the theory of fuzzy sets may well have a substantial impact on scientific methodology in the years ahead, particularly in the realms of psychology, economics, law, medicine, decision analysis, information retrieval, and artificial intelligence
The US-Japan Seminar on Fuzzy Sets was sponsored by the US-Japan Cooperative Science Program, with the joint support of the National Science Foundation and the Japan Society for the Promotion of Science In organizing the seminar, the co-chairmen received considerable help from JE O’Connell and L Trent of the National Science Foundation; the staff of the Japan Society for the Promotion of Science; and D J Angelakos and his staff at the University of California, Berkeley As co-editors of this volume, we wish also to express our heartfelt appreciation to Terry Brown for her invaluable assistance in the preparation of the manuscript, and to Academic Press for undertaking its publication
For the convenience of the reader, a brief introduction to the theory of fuzzy sets is provided in the Appendix of the first paper in this volume An up-to-date bibliography on fuzzy sets and their applications is included at the
end of the volume
812 citations