Search Results - (((((((ant OR west) OR mantis) OR win) OR cantor) OR anne) OR shape) OR hints) algorithms.

Intelligent Technologies : From Theory to Applications - The New Trends in Computational Intelligence. by Kvasnicka, V.

Table of Contents: “…Neural Networks; Superconvergence Concept in Machine Learning; Application of the Parallel Population Learning Algorithm to Training Feed-forward ANN; Markovian Architectural Bias of Recurrent Neural Networks; SOFM Training Speedup; Generalized Forecasting Sigma-Pi Neural Network; Human Centered Intelligent Robots Using "Ontological Neural Network"; Trajectory Bounds of Solutions of Delayed Cellular Neural Networks Differential Systems; From Plain to Modular Topology: Automatic Modularization of Structured ANNs.…”
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Bio-inspired computation in telecommunications

Table of Contents: “…Bio-inspired optimization algorithms; 1.4.1. SI-Based Algorithms; 1.4.1.1. Ant and bee algorithms; 1.4.1.2. …”
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Optimization and Inverse Problems in Electromagnetism. by Salvini, Alessandro

Table of Contents: “…Cover; EDITORIAL ADVISORY BOARD; Analytical and numerical models of the magnetoacoustic tomography with magnetic induction; Application of PSO and TLBO algorithms with neural network for optimal design of electrical machines; Comparison of three space mapping techniques on electromagnetic design optimization; Optimal shape design of multi layered IPMSM using adaptive dynamic Taylor Kriging model; Design by optimization of controlled static converters with EMC filters in aeronautics.…”
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Handbook of Mathematical Induction. by Gunderson, David S.

Table of Contents: “…Chapter 14: Logic and languageChapter 15: Graphs; Chapter 16: Recursion and algorithms; Chapter 17: Games and recreations; Chapter 18: Relations and functions; Chapter 19: Linear and abstract algebra; Chapter 20: Geometry; Chapter 21: Ramsey theory; Chapter 22: Probability and statistics; Part III: Solutions and hints to exercises; Chapter 23: Solutions: Foundations; Chapter 24: Solutions: Inductive techniques applied to the infinite; Chapter 25: Solutions: Paradoxes and sophisms; Chapter 26: Solutions: Empirical induction; Chapter 27: Solutions: Identities.…”
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Nonparametric Statistics and Mixture Models : a Festschrift in Honor of Thomas P Hettmansperger. by Hunter, David

Table of Contents: “…Estimation of Hazard Functions with Shape Restrictions Using Regression SplinesMultivariate Models and the First Four Moments; An Empirical Study of Indirect Cross-Validation; Extensions of Reliability Theory; Rank Regression under Possible Model Misspecification; Iterative Conditional Maximization Algorithm for Nonconcave Penalized Likelihood; AUTHOR INDEX.…”
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Artificial intelligence : approaches, tools, and applications

Table of Contents: “…EVOLUTIONARY COMPUTING ; 2.1. Genetic Algorithms ; 2.2. Mechanism of a Genetic Algorithm ; 3. …”
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Evolutionary optimization

Table of Contents: “…Cover -- Contents -- Preface -- Contributing Authors -- Part I Introduction -- 1 Conventional Optimization Techniques -- 1 Classifying Optimization Models -- 2 Linear Programming -- 3 Goal Programming -- 4 Integer Programming -- 5 Nonlinear Programming -- 6 Simulation -- 7 Further Reading -- 2 Evolutionary Computation -- 1 What Is Evolutionary Computation -- 2 A Brief Overview of Evolutionary Computation -- 3 Evolutionary Algorithm and Generate-and-Test Search Algorithm -- 4 Search Operators -- 5 Summary -- Part II Single Objective Optimization -- 3 Evolutionary Algorithms and Constrained Optimization -- 1 Introduction -- 2 General considerations -- 3 Numerical optimization -- 4 Final Remarks -- 4 Constrained Evolutionary Optimization -- 1 Introduction -- 2 The Penalty Function Method -- 3 Stochastic Ranking -- 4 Global Competitive Ranking -- 5 How Penalty Methods Work -- 6 Experimental Study -- 7 Conclusion -- Appendix: Test Function Suite -- Part III Multi-Objective Optimization -- 5 Evolutionary Multiobjective Optimization -- 1 Introduction -- 2 Definitions -- 3 Historical Roots -- 4 A Quick Survey of EMOO Approaches -- 5 Current Research -- 6 Future Research Paths -- 7 Summary -- 6 MEA for Engineering Shape Design -- 1 Introduction -- 2 Multi-Objective Optimization and Pareto-Optimality -- 3 Elitist Non-dominated Sorting GA (NSGA-II) -- 4 Hybrid Approach -- 5 Optimal Shape Design -- 6 Simulation Results -- 7 Conclusion -- 7 Assessment Methodologies for MEAs -- 1 Introduction -- 2 Assessment Methodologies -- 3 Discussion -- 4 Comparing Two Algorithms: An Example -- 5 Conclusions and Future Research Paths -- Part IV Hybrid Algorithms -- 8 Hybrid Genetic Algorithms -- 1 Introduction -- 2 Hybridizing GAs with Local Improvement Procedures -- 3 Adaptive Memory GA's -- 4 Summary -- 9 Combining choices of heuristics -- 1 Introduction -- 2 GAs and parameterised algorithms -- 3 Job Shop Scheduling -- 4 Scheduling chicken catching -- 5 Timetabling -- 6 Discussion and future directions -- 10 Nonlinear Constrained Optimization -- 1 Introduction -- 2 Previous Work -- 3 A General Framework to look for SPdn -- 4 Experimental Results -- 5 Conclusions -- Part V Parameter Selection in EAs -- 11 Parameter Selection -- 1 Introduction -- 2 Parameter tuning vs. parameter control -- 3 An example -- 4 Classification of Control Techniques -- 5 Various forms of control -- 6 Discussion -- Part VI Application of EAs to Practical Problems -- 12 Design of Production Facilities -- 1 Introduction -- 2 Design for Material Flow When the Number of I/O Points is Unconstrained -- 3 Design for Material Flow for a Single I/O Point -- 4 Considering Intradepartmental Flow -- 5 Material Handling System Design -- 6 Concluding Remarks -- 13 Virtual Population and Acceleration Techniques -- 1 Introduction -- 2 Concept of Virtual Population -- 3 Solution Acceleration Techniques -- 4 Accelerated GA and Acceleration Sche.…”
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Equity in Instructional Technologies by Watulak, Sarah Lohnes

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Computer Aided Methods in Optimal Design and Operations : Vilnius, Lithuania. by Bogle, I. D. L.

Table of Contents: “…General Branch and Bound Algorithm.…”
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Mathematical morphology and its applications to image and signal processing

Table of Contents: “…Geodesic Set Definition in Case of Empty Intersection -- The Morphological-Affine Object Deformation -- Affine Invariant Mathematical Morphology Applied to a Generic Shape Recognition Algorithm -- Filtering -- Folding Induced Self-Dual Filters -- Flexible Linear Openings and Closings -- Some Applications of Aperture Filters -- GA Optimisation of Multidimensional Grey-Scale Soft Morphological Filters with Applications in Archive Film Restoration -- Connectivity and Connected Operators -- New Insight on Digital Topology -- Approximate Connectivity and Mathematical Morphology -- Multiresolution Connectivity: An Axiomatic Approach -- Connected Operators Based on Region-Tree Pruning -- Segmentation -- Image Segmentation Based on the Derivative of the Morphological Profile -- Flooding and Segmentation -- A Morphological Multi-Scale Gradient for Color Image Segmentation -- Automatic Watershed Segmentation of Color Images -- Motion Segmentation using Seeded Region Growing -- A Segmentation Pyramid for the Interactive Segmentation of 3-D Images and Video Sequences -- Partition Lattice Operators for Extraction of Semantic Video Objects -- Texture Analysis -- Morphological Granulometric Deconstruction -- Surface Texture Classification from Morphological Transformations -- Content Dependent Image Sampling using Mathematical Morphology: Application to Mipmapping -- Multiresolution Techniques and Scale-Spaces -- Morphological Pyramids and Wavelets Based on the Quincunx Lattice -- Morphological Scale-Space Operators: An Algebraic Framework -- An Idempotent Scale-Space Approach for Morphological Segmentation -- Algorithms -- Efficient Dilation, Erosion, Opening and Closing Algorithms -- Fast Morphological Attribute Operations using Tarjan's Union-Find Algorithm -- A Change Detector Based on Level Sets -- A General Algorithm for Computing Distance Transforms in Linear Time -- The Ordered Queue and the Optimality of the Watershed Approaches -- Discrete 3D Wave Propagation for Computing Morphological Operations from Surface Patches and Unorganized Points -- Applications -- Two-Stage Lossy/Lossless Compression of Grayscale Document Images -- Boosting OCR Classifier by Optimal Edge Noise Filtering -- Morphological Bank Check Logo Segmentation with Few a Priori Knowledge -- Morphological Pseudo Convex Hull to Correct Handwriting Baseline Skew -- Morphological Segmentation of Text and Figures in Renaissance Books (XVI Century) -- Application of Mathematical Morphology and Markov Random Field Theory to the Automatic Extraction of Linear Features in Airborne Images -- Testing Some Morphological Approaches to Face Localization -- Quantitative Description of Telecommunication Networks by Simulation -- Author Index.…”
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Metaheuristics for intelligent electrical networks by Héliodore, Frédéric, Nakib, Amir, Ismail, Boussaad, Ouchraa, Salma, Schmitt, Laurent

Table of Contents: “…2.2.1. Genetic algorithms2.2.2. Evolution strategies; 2.2.3. Coevolutionary algorithms; 2.2.4. …”
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Advances in mechatronics, robotics and automation II : selected, peer reviewed papers from the 2014 2nd International Conference on Mechatronics, Robotics and Automation (ICMRA 201...

Table of Contents: “…A New Color Fidelity Fast Denoising Algorithm for High-ISO ImagesA New Video Denoising Algorithm Based on Adaptive Polyview Fusion; A Parallel Stereo Matching Algorithm Core for FPGA Modeled by DSP Builder; A Robust Algorithm for the Inspection of Fastener Head; A Video Compression Coding Algorithm for Network Content Transmission; An Efficient Shape Recognition Method Using Multiple Description Fusion; Clonal Selection Algorithm Based on Manifold Distance for Image Compression; Dot Area Coverage Percentage Measurement Method for Plate Microscopic Image Based on Color Segmentation.…”
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Soft Computing in Economics and Business. by Linares-Mustarós, Salvador

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Mixed Models : Theory and Applications with R. by Demidenko, Eugene

Table of Contents: “…2.9 Derivatives of the log-likelihood function -- 2.10 Newton-Raphson algorithm -- 2.11 Fisher scoring algorithm -- 2.11.1 Simplified FS algorithm -- 2.11.2 Empirical FS algorithm -- 2.11.3 Variance-profile FS algorithm -- 2.12 EM algorithm -- 2.12.1 Fixed-point algorithm -- 2.13 Starting point -- 2.13.1 FS starting point -- 2.13.2 FP starting point -- 2.14 Algorithms for restricted MLE -- 2.14.1 Fisher scoring algorithm -- 2.14.2 EM algorithm -- 2.15 Optimization on nonnegative definite matrices -- 2.15.1 How often can one hit the boundary? …”
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Special issue on the discreet element method : numerical modeling of discontinue. by Kuhn, Matthew R.

Table of Contents: “…Comparison of experimental and FEM/DEM results for gravitational deposition of identical cubesA 2D polygon/polygon contact model: algorithmic aspects; Selecting a suitable time step for discrete element simulations that use the central difference time integration scheme; Numerical studies of uniaxial powder compaction process by 3D DEM; Thin tunnel liners modelled with particle flow code; Shape selection menu for grand scale discontinua systems.…”
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Introduction to imprecise probabilities

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The finite element method : its basis and fundamentals by Zienkiewicz, O. C.

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Optimal structural design : contact problems and high-speed penetration by Banichuk, Nikolaĭ Vladimirovich, Ivanova, S. I︠U︡. (Svetlana I︠U︡rʹevna)

Table of Contents: “…Shape optimization of punch moving with friction; 4. …”
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Recent Advances in Optimization and Inverse Problems in Electromagnetism by Sykulski, Jan

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Data Analytics in Bioinformatics : A Machine Learning Perspective. by Satpathy, Rabinarayan

Table of Contents: “…3.4.1 Comparative Analysis of ANN With Broadly Used Traditional ML Algorithms -- 3.5 Critical Analysis -- 3.6 Conclusion -- References -- Part 2: MACHINE LEARNING AND GENOMIC TECHNOLOGY, FEATURE SELECTION AND DIMENSIONALITY REDUCTION -- 4 Dimensionality Reduction Techniques: Principles, Benefits, and Limitations -- 4.1 Introduction -- 4.2 The Benefits and Limitations of Dimension Reduction Methods -- 4.3 Components of Dimension Reduction -- 4.3.1 Feature Selection -- 4.3.2 Feature Reduction -- 4.4 Methods of Dimensionality Reduction -- 4.4.1 Principal Component Analysis (PCA)…”
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