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Format: 1 online resource (631 pages) : , illustrations (some color), tables

ISBN: 0-12-811319-7 , 0-12-811318-9

Note: Front Cover -- Handbook of Neural Computation -- Copyright -- Contents -- Contributors -- About the Editors -- 1 Gravitational Search Algorithm With Chaos -- 1.1 Introduction -- 1.2 Gravitational Search Algorithm -- 1.3 Chaotic Maps for GSA -- 1.3.1 Chaotic Maps -- 1.3.2 Integrating Chaotic Maps With GSA -- 1.4 Experimental Results and Discussion -- 1.4.1 Search Performance Analysis -- 1.4.2 Convergence Analysis -- 1.5 CGSA for Engineering Design Problems -- 1.5.1 Welded Beam Design -- 1.5.2 Pressure Vessel Design -- 1.6 Conclusion -- References -- 2 Textures and Rough Sets -- 2.1 Introduction -- 2.2 Fuzzy Lattices -- The Unit Interval [0, 1] -- 2.3 Texture Spaces -- Products -- Fuzzy Set Texture -- Direlations -- The Composition of Direlations -- Complemented Direlations -- Sections and Presections -- 2.4 Rough Sets -- Textures and Rough Sets -- 2.5 De nability -- 2.6 Order Preserving Functions -- 2.7 Approximation Spaces and Information Systems -- 2.8 Conclusion -- References -- 3 Hydrological Time Series Forecasting Using Three Different Heuristic Regression Techniques -- 3.1 Introduction -- 3.2 Methods -- 3.2.1 Least-Square Support Vector Regression -- 3.2.2 Multivariate Adaptive Regression Spline -- 3.2.3 M5 Model Tree -- 3.3 Applications and Results -- 3.4 Conclusion -- References -- 4 A Re ection on Image Classi cations for Forest Ecology Management: Towards Landscape Mapping and Monitoring -- 4.1 Introduction -- 4.2 Background -- 4.2.1 De nitions in Remote Sensing Community -- 4.2.2 Importance of Using Remote Sensing Tools -- 4.2.3 Types of Remote Sensing Data -- 4.2.4 Uncertainty Assessments of Remote Sensing Data -- 4.2.4.1 Thematic Data Collection in Field -- 4.2.4.2 Classi cation Accuracy of LULC Maps -- 4.3 Image Classi cation for Forest Cover Mapping -- 4.3.1 Mapping Methodologies -- 4.3.1.1 Approaches with Manual Interpretation. , 4.3.1.2 Approaches Relying on Automated Classi cation -- 4.3.1.2.1 Parametric Methods -- 4.3.1.2.2 Non-parametric Methods -- 4.3.2 Emerging Ensemble Classi ers -- 4.3.2.1 Random Forest Classi ers -- 4.3.2.1.1 From Multi-spectral, LiDAR, and Radar Remote Sensing Data -- 4.3.2.1.2 From Hyperspectral Data -- 4.3.2.1.3 From Multi-source Data -- 4.4 A Case Study in the Himalayan Region -- 4.5 Future Research Outlook -- References -- 5 An Intelligent Hybridization of ABC and LM Algorithms With Constraint Engineering Applications -- 5.1 Introduction -- 5.2 Brief Introduction of Optimization Methods -- 5.2.1 Arti cial Bee Colony Algorithm -- 5.2.2 Levenberg-Marquardt Algorithm -- 5.2.3 Proposed Hybrid Algorithm: ABC-LM -- 5.3 Numerical Results -- 5.3.1 Unconstrained Optimization of Benchmark Functions -- 5.3.1.1 Comparisons of ABC, LM, and ABC-LM Algorithms -- 5.3.1.2 Comparisons with Literature Works -- 5.3.2 Constrained Real-World Optimization Problems -- 5.3.2.1 Welded Beam Design -- 5.3.2.2 Pressure Vessel Design -- 5.3.2.3 Tension/Compression Spring Design -- 5.3.2.4 Multi-tool Turning Operation -- 5.4 Strengths and Limitations -- 5.5 Conclusion -- References -- 6 Network Intrusion Detection Model Based on Fuzzy-Rough Classi ers -- 6.1 Introduction -- Misuse Detection System -- Anomaly Based Intrusion Detection System -- 6.2 Related Work -- 6.3 Methodology -- K-Nearest Neighbor (KNN) -- 6.3.1 Fuzzy Set Theory -- 6.3.2 Rough Set Theory -- Example -- Example -- 6.3.3 Hybridization of Fuzzy-Rough Set Theory -- 6.3.4 Fuzzy Nearest Neighbor (FNN) Classi cation -- 6.3.5 Fuzzy-Rough Nearest Neighbor Algorithm (FRNN) -- 6.3.6 Fuzzy Ownership Algorithm -- 6.3.7 Vaguely Quanti ed Nearest Neighbors (VQNN) -- 6.3.8 Ordered Weighted Average Nearest Neighbors -- 6.4 Experimental Setup -- 6.4.1 NSL-KDD Data Set -- 6.4.2 Feature Selection Process -- Ant Search. , Random Search -- 6.4.3 Confusion Matrix -- 6.4.4 Cross-Validation -- 6.5 Result Analysis -- 6.6 Conclusions -- References -- 7 Ef cient System Reliability Analysis of Earth Slopes Based on Support Vector Machine Regression Model -- 7.1 Introduction -- 7.2 Adopted Methodologies -- 7.2.1 Deterministic Slope Stability Analysis -- 7.2.2 Reliability Analysis of Slope Using Critical Slip Surfaces -- 7.2.3 System Reliability Analysis of Slopes Using SVM-Based MCS -- 7.2.3.1 Support Vector Machine (SVM) Regression -- 7.2.3.2 Proposed Algorithm for System Reliability Analysis Using SVR-Based MCS -- 7.3 Illustrative Example and Results -- 7.3.1 Slope Description -- 7.3.2 Deterministic Analyses and Results -- 7.3.3 Reliability Analyses and Results -- 7.3.3.1 Reliability Analyses Based on the Probabilistic Critical Slip Surfaces -- 7.3.3.2 System Reliability Analyses -- 7.4 Summary and Conclusions -- Acknowledgments -- References -- 8 Predicting Short-Term Congested Traf c Flow on Urban Motorway Networks -- 8.1 Introduction -- 8.2 Literature Review -- 8.3 Research Methodology -- 8.3.1 Back-Propagation Neural Network -- Stochastic Gradient Descent -- 8.3.2 Neuro-Fuzzy -- Fuzzy Logic -- Neuro Fuzzy -- 8.3.2.1 Input Variable Unit -- 8.3.2.2 Parameter Tuning Unit -- 8.3.2.3 Traf c Flow Estimator -- 8.3.3 Deep Learning -- Deep Belief Networks -- 8.3.4 Random Forest -- 8.4 Performance Evaluation -- 8.4.1 Error Measurement -- 8.4.2 Model Sensitivity -- 8.5 Application Results -- 8.5.1 Error Measurement -- 8.5.2 Rank Sensitivity -- 8.6 Conclusions and Future Works -- Acknowledgments -- References -- 9 Object Categorization Using Adaptive Graph-Based Semi-supervised Learning -- 9.1 Introduction -- 9.2 Related Work: Graph Construction Methods -- K-Nearest Neighbor Method -- e-Ball Graph Method -- LLE Graph Construction -- l1 Graph Construction -- 9.3 Local Binary Patterns. , 9.4 Proposed LBP Graph Construction -- 9.4.1 Weighted Regularized Least Square Minimization -- 9.4.2 Two Phase WRLS (TPWRLS) -- 9.4.3 Difference Between TPWRLS Graph and Existing Graphs -- 9.5 Performance Evaluation -- 9.5.1 Graph-Based Label Propagation -- 9.5.2 Experimental Results -- 9.5.2.1 First group -- Computation Time -- 9.5.2.2 Second Group -- 9.6 Conclusion -- Acknowledgments -- References -- 10 Hemodynamic Model Inversion by Iterative Extended Kalman Smoother -- 10.1 Introduction -- 10.1.1 Outline -- 10.2 Methods -- 10.2.1 Extended Kalman Filter -- 10.2.2 Extended Kalman Smoother -- 10.2.3 Iteration Step -- 10.3 Dynamic Representation of the Hemodynamic Model System -- 10.4 Results -- 10.4.1 Simulation Results -- 10.4.1.1 Filtering Performance Overview -- 10.4.1.2 Iteration and Smoother Performance -- 10.4.1.3 IEKS Simulations -- 10.4.2 Discussion -- 10.5 Real Data Case Study -- 10.5.1 Test Procedure and Real Data -- 10.5.1.1 IEKS Method With Switched Parameter Variance -- 10.5.2 Discussion -- 10.5.2.1 Validation of the Results -- 10.5.2.2 Estimated Hemodynamic Variables -- 10.6 Conclusion -- Acknowledgments -- References -- 11 Improved Sparse Approximation Models for Stochastic Computations -- 11.1 Introduction -- 11.2 Fundamentals of HDMR, Kriging, LASSO, LAR, and FS -- 11.2.1 High-Dimensional Model Representation -- 11.2.2 Kriging -- 11.2.3 Least Absolute Shrinkage and Selection Operator (LASSO) -- 11.2.4 Least Angle Regression (LAR) -- 11.2.5 Forward Selection (FS) -- 11.3 Proposed Approaches -- 11.3.1 Proposed Approach 1 (PA1) -- 11.3.2 Proposed Approach 2 (PA2) -- 11.3.3 Proposed Approach 3 (PA3) -- 11.4 Numerical Examples -- 11.4.1 Problem Set 1: Analytical Test Functions -- 11.4.2 Problem Set 2: Practical Problems -- 11.4.2.1 Five-Storey Building Subjected to Lateral Load -- 11.4.2.2 Twenty-Five Bar Space Truss. , 11.5 Summary and Conclusions -- References -- 12 Symbol Detection in Multiple Antenna Wireless Systems via Ant Colony Optimization -- 12.1 Introduction -- 12.2 Overview of Ant Colony Optimization -- 12.3 Point-to-Point MIMO System Model -- 12.4 Traditional MIMO Detection Techniques -- 12.4.1 Zero Forcing Detector -- 12.4.2 Minimum Mean Squared Error Detector -- 12.4.3 Successive Interference Cancellation Detector -- 12.5 Ant Colony Optimization Based MIMO Detection -- 12.6 Results and Discussion -- 12.7 Conclusions -- References -- 13 Application of Particle Swarm Optimization to Solve Robotic Assembly Line Balancing Problems -- 13.1 Introduction -- 13.2 Straight Robotic Assembly Line Balancing Problems -- 13.2.1 Assumptions and Mathematical Model for Balancing Straight Robotic Assembly Line -- 13.2.2 Particle Swarm Optimization for Solving Straight Robotic Assembly Line Balancing Problems -- 13.2.2.1 Implementation of PSO -- Recursive Allocation Method (Cycle Time Calculation) -- Consecutive Allocation Method (Cycle Time Calculation) -- 13.2.3 Parameter Selection for Straight RALB Problem -- 13.2.4 Computational Results for Straight Robotic Assembly Line Balancing Problems -- 13.3 Robotic U-Shaped Assembly Line Balancing Problems -- 13.3.1 Assumptions and Mathematical Model for Balancing U-Shaped Robotic Assembly Line -- 13.3.2 Particle Swarm Optimization for Solving Robotic U-Shaped Assembly Line Balancing Problems -- 13.3.3 Differences Between Straight and U-Shaped Robotic Assembly Lines -- 13.3.4 Parameters for PSO to Solve RUALB Problems -- 13.3.5 Computational Results for RUALB Problems -- 13.4 Summary -- References -- 14 The Cuckoo Optimization Algorithm and Its Applications -- 14.1 Introduction -- 14.2 Cuckoo Optimization Algorithm -- 14.3 Applications of the Cuckoo Optimization Algorithm -- 14.3.1 Replacement of Obsolete Components. , 14.3.2 Machining Parameters.

Language: English