Is there a need for fuzzy logic

Part I: Background and Fundamentals Introduction, Mohamed Gad-el-Hak, University of Notre Dame Scaling of Micromechanical Devices, William Trimmer, Standard MEMS, Inc., and Robert H. Stroud, Aerospace Corporation Mechanical Properties of MEMS Materials, William N. Sharpe, Jr., Johns Hopkins University Flow Physics, Mohamed Gad-el-Hak, University of Notre Dame Integrated Simulation for MEMS: Coupling Flow-Structure-Thermal-Electrical Domains, Robert M. Kirby and George Em Karniadakis, Brown University, and Oleg Mikulchenko and Kartikeya Mayaram, Oregon State University Liquid Flows in Microchannels, Kendra V. Sharp and Ronald J. Adrian, University of Illinois at Urbana-Champaign, Juan G. Santiago and Joshua I. Molho, Stanford University Burnett Simulations of Flows in Microdevices, Ramesh K. Agarwal and Keon-Young Yun, Wichita State University Molecular-Based Microfluidic Simulation Models, Ali Beskok, Texas A&M University Lubrication in MEMS, Kenneth S. Breuer, Brown University Physics of Thin Liquid Films, Alexander Oron, Technion, Israel Bubble/Drop Transport in Microchannels, Hsueh-Chia Chang, University of Notre Dame Fundamentals of Control Theory, Bill Goodwine, University of Notre Dame Model-Based Flow Control for Distributed Architectures, Thomas R. Bewley, University of California, San Diego Soft Computing in Control, Mihir Sen and Bill Goodwine, University of Notre Dame Part II: Design and Fabrication Materials for Microelectromechanical Systems Christian A. Zorman and Mehran Mehregany, Case Western Reserve University MEMS Fabrication, Marc J. Madou, Nanogen, Inc. LIGA and Other Replication Techniques, Marc J. Madou, Nanogen, Inc. X-Ray-Based Fabrication, Todd Christenson, Sandia National Laboratories Electrochemical Fabrication (EFAB), Adam L. Cohen, MEMGen Corporation Fabrication and Characterization of Single-Crystal Silicon Carbide MEMS, Robert S. Okojie, NASA Glenn Research Center Deep Reactive Ion Etching for Bulk Micromachining of Silicon Carbide, Glenn M. Beheim, NASA Glenn Research Center Microfabricated Chemical Sensors for Aerospace Applications, Gary W. Hunter, NASA Glenn Research Center, Chung-Chiun Liu, Case Western Reserve University, and Darby B. Makel, Makel Engineering, Inc. Packaging of Harsh-Environment MEMS Devices, Liang-Yu Chen and Jih-Fen Lei, NASA Glenn Research Center Part III: Applications of MEMS Inertial Sensors, Paul L. Bergstrom, Michigan Technological University, and Gary G. Li, OMM, Inc. Micromachined Pressure Sensors, Jae-Sung Park, Chester Wilson, and Yogesh B. Gianchandani, University of Wisconsin-Madison Sensors and Actuators for Turbulent Flows. Lennart Loefdahl, Chalmers University of Technology, and Mohamed Gad-el-Hak, University of Notre Dame Surface-Micromachined Mechanisms, Andrew D. Oliver and David W. Plummer, Sandia National Laboratories Microrobotics Thorbjoern Ebefors and Goeran Stemme, Royal Institute of Technology, Sweden Microscale Vacuum Pumps, E. Phillip Muntz, University of Southern California, and Stephen E. Vargo, SiWave, Inc. Microdroplet Generators. Fan-Gang Tseng, National Tsing Hua University, Taiwan Micro Heat Pipes and Micro Heat Spreaders, G. P. "Bud" Peterson, Rensselaer Polytechnic Institute Microchannel Heat Sinks, Yitshak Zohar, Hong Kong University of Science and Technology Flow Control, Mohamed Gad-el-Hak, University of Notre Dame) Part IV: The Future Reactive Control for Skin-Friction Reduction, Haecheon Choi, Seoul National University Towards MEMS Autonomous Control of Free-Shear Flows, Ahmed Naguib, Michigan State University Fabrication Technologies for Nanoelectromechanical Systems, Gary H. Bernstein, Holly V. Goodson, and Gregory L. Snider, University of Notre Dame Index

The MEMS Handbook

The notion of fuzziness as defined in this paper relates to situations in which the source of imprecision is not a random variable or a stochastic process, but rather a class or classes which do not possess sharply defined boundaries, e.g., the “class of bald men,” or the “class of numbers which are much greater than 10,” or the “class of adaptive systems,” etc. A basic concept which makes it possible to treat fuzziness in a quantitative manner is that of a fuzzy set, that is, a class in which there may be grades of membership intermediate between full membership and non-membership. Thus, a fuzzy set is characterized by a membership function which assigns to each object its grade of membership (a number lying between 0 and 1) in the fuzzy set. After a review of some of the relevant properties of fuzzy sets, the notions of a fuzzy system and a fuzzy class of systems are introduced and briefly analyzed. The paper closes with a section dealing with optimization under fuzzy constraints in which an approach to...

Fuzzy sets and systems

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Infinite Abelian Groups

Studies in Fuzziness and Soft Computing

Topology has its roots in geometry and analysis. From a geometric point of view, topology was the study of properties preserved by a certain group of transformations, namely the homeomorphisms. Certain notions of topology are also abstractions of classical concepts in the study of real or complex functions. These concepts include open sets, continuity, connectedness, compactness, and metric spaces. They were a basic part of analysis before being generalized in topology.

Fuzzy Topological Spaces

Fuzzy Subsets: Fuzzy Relations.- Fuzzy Equivalence Relations.- Pattern Classification.- Similarity Relations.- References.- Fuzzy Graphs: Paths and Connectedness. Bridges and Cut Vertices. Forests and Trees. Trees and Cycles. A Characterization of Fuzzy Trees. (Fuzzy) Cut Sets. (Fuzzy Chords, (Fuzzy) Cotrees, and (Fuzzy) Twigs. (Fuzzy) 1-Chain with Boundary 0, (Fuzzy) Coboundary, and (Fuzzy) Cocycles. (Fuzzy) Cycle Set and (Fuzzy) Cocycle Set.- Fuzzy Line Graphs.- Fuzzy Interval Graphs. Fuzzy Intersection Graphs. Fuzzy Interval Graphs. The Fulkerson and Gross Characterization. The Gilmore and Hoffman Characterization.- Operations on Fuzzy Graphs: Cartesian Product and Composition. Union and Join.- On Fuzzy Tree Definition.- References.- Applications of Fuzzy Graphs: Clusters.- Cluster Analysis. Cohesiveness. Slicing in Fuzzy Graphs.- Application to Cluster Analysis.- Fuzzy Intersection Equations. Existence of Solutions.- Fuzzy Graphs in Database Theory. Representation of Dependency Structure r(X,Y) by Fuzzy Graphs.- A Description of Strengthening and Weakening Members of a Group. Connectedness Criteria. Inclusive Connectedness Categories. Exclusive Connectedness Categories.- An Application of Fuzzy Graphs to the Problem Concerning Group Structure. Connectedness of a Fuzzy Graph. Weakening and Strenghtening Points of a Fuzzy Directed Graph.- References.- Fuzzy Hypergraphs: Fuzzy Hypergraphs.- Fuzzy Transversals of Fuzzy Hypergraphs. Properties of Tr(H). Construction of H3.- Coloring of Fuzzy Hypergraphs. beta-degree Coloring Procedures. Chromatic Values of Fuzzy Colorings.- Intersecting Fuzzy Hypergraphs. Characterization of Strongly Intersecting Hypergraphs. Simply Ordered Intersecting Hypergraphs. H-dominant Transversals.- Hebbian Structures.- Additional Applications.- References.

Fuzzy Graphs and Fuzzy Hypergraphs

Cycles and cocycles of fuzzy graphs

1 Fuzzy Subsets.- 1.1 Fuzzy Relations.- 1.2 Operations on Fuzzy Relations.- 1.3 Reflexivity, Symmetry and Transitivity.- 1.4 Pattern Classification Based on Fuzzy Relations.- 1.5 Advanced Topics on Fuzzy Relations.- 1.6 References.- 2 Fuzzy Graphs.- 2.1 Paths and Connectedness.- 2.2 Clusters.- 2.3 Cluster Analysis and Modeling of Information Networks.- 2.4 Connectivity in Fuzzy Graphs.- 2.5 Application to Cluster Analysis.- 2.6 Operations on Fuzzy Graphs.- 2.7 Fuzzy Intersection Equations.- 2.8 Fuzzy Graphs in Database Theory.- 2.9 References.- 3 Fuzzy Topological Spaces.- 3.1 Topological Spaces.- 3.2 Metric Spaces and Normed Linear Spaces.- 3.3 Fuzzy Topological Spaces.- 3.4 Sequences of Fuzzy Subsets.- 3.5 F-Continuous Functions.- 3.6 Compact Fuzzy Spaces.- 3.7 Iterated Fuzzy Subset Systems.- 3.8 Chaotic Iterations of Fuzzy Subsets.- 3.9 Starshaped Fuzzy Subsets.- 3.10 References.- 4 Fuzzy Digital Topology.- 4.1 Introduction.- 4.2 Crisp Digital Topology.- 4.3 Fuzzy Connectedness.- 4.4 Fuzzy Components.- 4.5 Fuzzy Surroundedness.- 4.6 Components, Holes, and Surroundedness.- 4.7 Convexity.- 4.8 The Sup Projection.- 4.9 The Integral Projection.- 4.10 Fuzzy Digital Convexity.- 4.11 On Connectivity Properties of Grayscale Pictures.- 4.12 References.- 5 Fuzzy Geometry.- 5.1 Introduction.- 5.2 The Area and Perimeter of a Fuzzy Subset.- 5.3 The Height, Width and Diameter of a Fuzzy Subset.- 5.4 Distances Between Fuzzy Subsets.- 5.5 Fuzzy Rectangles.- 5.6 A Fuzzy Medial Axis Transformation Based on Fuzzy Disks.- 5.7 Fuzzy Triangles.- 5.8 Degree of Adjacency or Surroundedness.- 5.9 Image Enhancement and Thresholding Using Fuzzy Compactness.- 5.10 Fuzzy Plane Geometry: Points and Lines.- 5.11 Fuzzy Plane Geometry: Circles and Polygons.- 5.12 Fuzzy Plane Projective Geometry.- 5.13 A Modified Hausdorff Distance Between Fuzzy Subsets..- 5.14 References.- 6 Fuzzy Abstract Algebra.- 6.1 Crisp Algebraic Structures.- 6.2 Fuzzy Substructures of Algebraic Structures.- 6.3 Fuzzy Submonoids and Automata Theory.- 6.4 Fuzzy Subgroups, Pattern Recognition and Coding Theory.- 6.5 Free Fuzzy Monoids and Coding Theory.- 6.6 Formal Power Series, Regular Fuzzy Languages, and Fuzzy Automata.- 6.7 Nonlinear Systems of Equations of Fuzzy Singletons.- 6.8 Localized Fuzzy Subrings.- 6.9 Local Examination of Fuzzy Intersection Equations.- 6.10 More on Coding Theory.- 6.11 Other Applications.- 6.12 References.- List of Figures.- List of Tables.- List of Symbols.

Fuzzy Mathematics: An Introduction for Engineers and Scientists

In this paper, fuzzy theory has been applied to social network analysis (SNA). Social network analysis models meaningful relations that exist between entities as graph. These entities may be people, events, organizations, symbols in text, sounds in verbalizations, nations of the world and so on. However, the fuzzy graph can be very huge and thus the ability to arrive at meaningful conclusions in a timely fashion may be quite difficult. With this in mind, a method to consolidate the information content of the fuzzy graph is proposed. Since none of the existing fuzzy binary operations meet the requirements, a new fuzzy binary operation called consolidation operation is also introduced.

Premchand S. Nair

Papers

Fuzzy Topological Spaces

Fuzzy Graphs and Fuzzy Hypergraphs

Cycles and cocycles of fuzzy graphs

Fuzzy Mathematics: An Introduction for Engineers and Scientists

Data Mining Through Fuzzy Social Network Analysis