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This book comprises a selection of papers on new methods for analysis and design of hybrid intelligent systems using soft computing techniques from the IFSA 2007 World Congress, held in Cancun, Mexico, June 2007.

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.

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This book comprises a selection of papers from IFSA 2007 on new methods for analysis and design of hybrid intelligent systems using soft computing techniques. Soft Computing (SC) consists of several computing paradigms, including fuzzy logic, neural 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.

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Fuzzy Logic as the Logic of Natural Languages.- Fuzzy Logic as the Logic of Natural Languages.- I: Type-2 Fuzzy Logic: Theory and Applications.- A Method for Response Integration in Modular Neural Networks with Type-2 Fuzzy Logic for Biometric Systems.- Evolving Type-2 Fuzzy Logic Controllers for Autonomous Mobile Robots.- Adaptive Type-2 Fuzzy Logic for Intelligent Home Environment.- Interval Type-1 Non-singleton Type-2 TSK Fuzzy Logic Systems Using the Hybrid Training Method RLS-BP.- An Efficient Computational Method to Implement Type-2 Fuzzy Logic in Control Applications.- Building Fuzzy Inference Systems with the Interval Type-2 Fuzzy Logic Toolbox.- Evolutionary Computing for Topology Optimization of Type-2 Fuzzy Systems.- II: Fuzzy Clustering: Theory and Applications.- Comparison of the Performance of Seven Classifiers as Effective Decision Support Tools for the Cytodiagnosis of Breast Cancer: A Case Study.- MFCM for Nonlinear Blind Channel Equalization.- Fuzzy Rules Extraction from Support Vector Machines for Multi-class Classification.- Density Based Fuzzy Support Vector Machines for Multicategory Pattern Classification.- A Modified FCM Algorithm for Fast Segmentation of Brain MR Images.- Incorporation of Non-euclidean Distance Metrics into Fuzzy Clustering on Graphics Processing Units.- Fuzzy C-Means, Gustafson-Kessel FCM, and Kernel-Based FCM: A Comparative Study.- Improved Fuzzy C-Means Segmentation Algorithm for Images with Intensity Inhomogeneity.- III: Intelligent Identification and Control.- A Fuzzy-Neural Hierarchical Multi-model for Systems Identification and Direct Adaptive Control.- Robust Speed Controller Design Method Based on Fuzzy Control for Torsional Vibration Suppression in Two-Mass System.- Self-organizing Fuzzy Controller Based on Fuzzy Neural Network.- Decision Making Strategies for Real-Time Train Dispatch and Control.- Soft Margin Training for Associative Memories Implemented by Recurrent Neural Networks.- IV: Time Series Prediction.- Modular Neural Networks with Fuzzy Integration Applied for Time Series Forecasting.- Predicting Job Completion Time in a Wafer Fab with a Recurrent Hybrid Neural Network.- A Hybrid ANN-FIR System for Lot Output Time Prediction and Achievability Evaluation in a Wafer Fab.- M-Factor High Order Fuzzy Time Series Forecasting for Road Accident Data.- Fuzzy Time Series: A Realistic Method to Forecast Gross Domestic Capital of India.- Design of Modular Neural Networks with Fuzzy Integration Applied to Time Series Prediction.- V: Pattern Recognition.- Characterize the Parameters of Genetic Algorithms Based on Zernike Polynomials for Recovery of the Phase of Interferograms of Closed Fringes Using Hybrid Technique.- Rotated Coin Recognition Using Neural Networks.- Selected Problems of Intelligent Handwriting Recognition.- 3-D Object Recognition Using an Ultrasonic Sensor Array and Neural Networks.- Soft System for Road Sign Detection.- Nonlinear Neuro-fuzzy Network for Channel Equalization.- On the Possibility of Reliably Constructing a Decision Support System for the Cytodiagnosis of Breast Cancer.- Spatial Heart Simulation and Analysis Using Unified Neural Network.- A Method for Creating Ensemble Neural Networks Using a Sampling Data Approach.- Pattern Recognition Using Modular Neural Networks and Fuzzy Integral as Method for Response Integration.- VI: Evolutionary Computation.- A Differential Evolution Algorithm for Fuzzy Extension of Functions.- Use of Pareto-Optimal and Near Pareto-Optimal Candidate Rules in Genetic Fuzzy Rule Selection.- A Dissimilation Particle Swarm Optimization-Based Elman Network and Applications for Identifying and Controlling Ultrasonic Motors.- A Cultural Algorithm for Solving the Set Covering Problem.- Integration of Production and Distribution Planning Using a Genetic Algorithm in Supply Chain Management.- Bacteria Swarm Foraging Optimization for Dynamical Resource Allocation in a Multizone Temperature Experimentation Platform.- An Ant Colo

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