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Handbook of neural computation /
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Amsterdam :
Academic Press,
2017.
|
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 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 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.
- 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.
- 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.