| Description |
1 online resource |
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text txt rdacontent |
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computer c rdamedia |
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online resource cr rdacarrier |
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Application of particle swarm optimization to solve robotic assembly line balancing problems 14. The cuckoo optimization algorithm and its applications 15. Hybrid Intelligent Model Based on Least Squared Support Vector Regression and Artificial Bee Colony Optimization for Time Series Modeling and Forecasting Horizontal Displacement of Hydropower Dam 16. Modelling the axial capacity of bored piles using multi-objective feature selection, functional network and multivariate adaptive regression spline 17. Transient stability constrained optimal power flow using chaotic whale optimization algorithm 18. Slope Stability Evaluation Using Radial Basis Function Neural Network, Least Squares Support Vector Machines, and Extreme Learning Machine 19. Alternating Decision Trees 20. Scene Understanding Using Deep Learning 21. Deep Learning for Coral Classification 22. A Deep Learning Framework for Classifying Mysticete Sounds 23. |
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Unsupervised deep learning for data-driven reliability and risk analysis of engineered systems 24. Applying Machine Learning Algorithms in Landslide Susceptibility Assessments 25. MDHS-LPNN: A hybrid FOREX predictor model using a Legendre polynomial Neural Network with a Modified Differential Harmony Search technique 26. A Neural Model of Attention and Feedback for Computing Perceived Brightness in Vision 27. Support Vector Machine: Principles, Parameters and Applications 28. Evolving Radial Basis Function Networks using Moth-Flame Optimizer 29. Application of Fuzzy Methods in Power system Problems 30. Application of Particle Swarm Optimization Algorithm in Power system Problems 31. Optimum Design of Composite Steel-Concrete Floors Based on a Hybrid Genetic Algorithm 32. A Comparative Study of Image Segmentation Algorithms and Descriptors for Building Detection 33. Object-Oriented Random Forest for High Resolution Land Cover Mapping Using Quickbird-2 Imagery. |
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CIP data; resource not viewed. |
| Bibliography |
Includes bibliographical references and index. |
| Contents |
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. |
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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. |
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3.3 Applications and Results3.4 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. |
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4.3.1.2 Approaches Relying on Automated Classi cation4.3.1.2.1 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. |
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5.2.1 Arti cial Bee Colony Algorithm5.2.2 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. |
| Subject |
Neural networks (Computer science)
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Neural Networks, Computer |
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Réseaux neuronaux (Informatique)
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COMPUTERS -- General.
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Neural networks (Computer science)
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| Genre/Form |
Electronic books.
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Handbook |
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handbooks.
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Handbooks and manuals
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Handbooks and manuals.
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Guides et manuels.
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| Added Author |
Samui, Pijush, 1978- editor.
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Roy, Sanjiban Sekhar, editor.
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Balas, Valentina Emilia, editor.
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| Other Form: |
Print version : 9780128113189 |
| ISBN |
9780128113196 (ePub ebook) |
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0128113197 (ePub ebook) |
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9780128113189 (pbk.) |
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0128113189 |
| Standard No. |
AU@ 000061153914 |
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AU@ 000066135563 |
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CHBIS 011069576 |
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CHNEW 001014454 |
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CHVBK 499780582 |
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GBVCP 897845862 |
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UKMGB 018438884 |
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