Analysis and Design of Intelligent Systems Using Soft Computing Techniques

1. Front Matter

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2. Fuzzy Logic as the Logic of Natural Languages

   1. Fuzzy Logic as the Logic of Natural Languages

      • Lotfi A. Zadeh

      Pages 1-2

3. Part I: Type-2 Fuzzy Logic: Theory and Applications

   1. Front Matter

      Pages 3-3

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   2. A Method for Response Integration in Modular Neural Networks with Type-2 Fuzzy Logic for Biometric Systems

      • Jérica Urías, Denisse Hidalgo, Patricia Melin, Oscar Castillo

      Pages 5-15

   3. Evolving Type-2 Fuzzy Logic Controllers for Autonomous Mobile Robots

      • Christian Wagner, Hani Hagras

      Pages 16-25

   4. Adaptive Type-2 Fuzzy Logic for Intelligent Home Environment

      • Sunghoi Huh, Seung-Eun Yang, Kwang-Hyun Park, JunHyeong Do, Hyoyoung Jang, Z. Zenn Bien

      Pages 26-35

   5. Interval Type-1 Non-singleton Type-2 TSK Fuzzy Logic Systems Using the Hybrid Training Method RLS-BP

      • G. M. Mendez

      Pages 36-44

   6. An Efficient Computational Method to Implement Type-2 Fuzzy Logic in Control Applications

      • Roberto Sepúlveda, Oscar Castillo, Patricia Melin, Oscar Montiel

      Pages 45-52

   7. Building Fuzzy Inference Systems with the Interval Type-2 Fuzzy Logic Toolbox

      • J. R. Castro, Oscar Castillo, Patricia Melin, L. G. Martínez, S. Escobar, I. Camacho

      Pages 53-62

   8. Evolutionary Computing for Topology Optimization of Type-2 Fuzzy Systems

      • Oscar Castillo, Alma Isabel Martinez, Alma Cristina Martinez

      Pages 63-75

4. Part II: Fuzzy Clustering: Theory and Applications

   1. Front Matter

      Pages 77-77

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   2. Comparison of the Performance of Seven Classifiers as Effective Decision Support Tools for the Cytodiagnosis of Breast Cancer: A Case Study

      • Nicandro Cruz-Ramírez, Héctor-Gabriel Acosta-Mesa, Humberto Carrillo-Calvet, Rocío-Erandi Barrientos-Martínez

      Pages 79-87

   3. MFCM for Nonlinear Blind Channel Equalization

      • Soowhan Han, Sungdae Park, Witold Pedrycz

      Pages 88-98

   4. Fuzzy Rules Extraction from Support Vector Machines for Multi-class Classification

      • Adriana da Costa F. Chaves, Marley Maria B. R. Vellasco, Ricardo Tanscheit

      Pages 99-108

   5. Density Based Fuzzy Support Vector Machines for Multicategory Pattern Classification

      • Frank Chung-Hoon Rhee, Jong Hoon Park, Byung In Choi

      Pages 109-118

   6. A Modified FCM Algorithm for Fast Segmentation of Brain MR Images

      • L. Szilágyi, S. M. Szilágyi, Z. Benyó

      Pages 119-127

   7. Incorporation of Non-euclidean Distance Metrics into Fuzzy Clustering on Graphics Processing Units

      • Derek Anderson, Robert H. Luke, James M. Keller

      Pages 128-139

   8. Fuzzy C-Means, Gustafson-Kessel FCM, and Kernel-Based FCM: A Comparative Study

      • Daniel Graves, Witold Pedrycz

      Pages 140-149

   9. Improved Fuzzy C-Means Segmentation Algorithm for Images with Intensity Inhomogeneity

      • Qingjie Zhao, Jingjing Song, Yueyin Wu

      Pages 150-159

5. Part III: Intelligent Identification and Control

   1. Front Matter

      Pages 161-161

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This book comprises a selection of papers from IFSA 2007 on new methods for ana- sis and design of hybrid intelligent systems using soft computing techniques. Soft Computing (SC) consists of several computing paradigms, including fuzzy logic, n- ral networks, and genetic algorithms, which can be used to produce powerful hybrid intelligent systems for solving problems in pattern recognition, time series prediction, intelligent control, robotics and automation. Hybrid intelligent systems that combine several SC techniques are needed due to the complexity and high dimensionality of real-world problems. Hybrid intelligent systems can have different architectures, which have an impact on the efficiency and accuracy of these systems, for this reason it is very important to optimize architecture design. The architectures can combine, in different ways, neural networks, fuzzy logic and genetic algorithms, to achieve the ultimate goal of pattern recognition, time series prediction, intelligent control, or other application areas. This book is intended to be a major reference for scientists and engineers interested in applying new computational and mathematical tools to design hybrid intelligent systems. This book can also be used as a reference for graduate courses like the f- lowing: soft computing, intelligent pattern recognition, computer vision, applied ar- ficial intelligence, and similar ones. The book is divided in to twelve main parts. Each part contains a set of papers on a common subject, so that the reader can find similar papers grouped together.

• agents
• automation
• calculus
• cognition
• control
• evolution
• evolutionary computation
• fuzzy
• fuzzy logic
• intelligent systems
• modeling
• neural networks
• pattern recognition
• robot
• robotics

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