Search Results - (((((((ant OR wikant) OR wants) OR arts) OR cantor) OR anne) OR maarten) OR hints) algorithms.
Suggested Topics within your search.
Suggested Topics within your search.
- Data processing 46
- Mathematical models 42
- Machine learning 35
- Artificial intelligence 33
- Mathematical optimization 28
- Mathematics 24
- artificial intelligence 21
- Data mining 20
- Algorithms 18
- Computer networks 17
- algorithms 16
- Artificial Intelligence 14
- Digital techniques 13
- History 12
- Neural networks (Computer science) 12
- Python (Computer program language) 12
- Computer simulation 11
- Data Mining 11
- Development 11
- Machine Learning 11
- Security measures 11
- Application software 10
- Electronic data processing 10
- Information technology 10
- Management 10
- Bioinformatics 9
- Computer algorithms 9
- Philosophy 9
- Social aspects 9
- Technological innovations 9
Search alternatives:
- ant »
- wikant »
- wants »
- cantor »
- arts »
-
341
-
342
Handbook of functional equations : functional inequalities
Published 2014Full text (MFA users only)
Electronic eBook -
343
Ranking the Liveability of the World's Major Cities : the Global Liveable Cities Index (GLCI).
Published 2012Full text (MFA users only)
Electronic eBook -
344
Power Quality : Problems and Mitigation Techniques.
Published 2014Table of Contents: “…Active Shunt Compensation; 4.1 Introduction; 4.2 State of the Art on DSTATCOMs; 4.3 Classification of DSTATCOMs; 4.3.1 Converter-Based Classification; 4.3.2 Topology-Based Classification; 4.3.3 Supply System-Based Classification; 4.3.3.1 Two-Wire DSTATCOMs; 4.3.3.2 Three-Wire DSTATCOMs; 4.3.3.3 Four-Wire DSTATCOMs; 4.4 Principle of Operation and Control of DSTATCOMs; 4.4.1 Principle of Operation of DSTATCOMs; 4.4.2 Control of DSTATCOMs; 4.4.2.1 Unit template- or PI Controller-Based Control Algorithm of DSTATCOMs.…”
Full text (MFA users only)
Electronic eBook -
345
FX barrier options : a comprehensive guide for industry quants
Published 2015Table of Contents: “…Stupid; 4.10 Five things we want from a model; 4.11 Stochastic volatility (SV) models; 4.11.1 SABR model; 4.11.2 Heston model; 4.12 Mixed local/stochastic volatility (lsv) models; 4.12.1 Term structure of volatility of volatility; 4.13 Other models and methods; 4.13.1 Uncertain Volatility (UV) models; 4.13.2 Jump-diffusion models; 4.13.3 Vanna-volga methods; 5 Smile Risk Management; 5.1 Black-Scholes with term structure; 5.2 Local volatility model; 5.3 Spot risk under smile models; 5.4 Theta risk under smile models; 5.5 Mixed local/stochastic volatility models; 5.6 Static hedging; 5.7 Managing risk across businesses; 6 Numerical Methods; 6.1 Finite-difference (FD) methods; 6.1.1 Grid geometry; 6.1.2 Finite-difference schemes; 6.2 Monte Carlo (MC) methods; 6.2.1 Monte Carlo schedules; 6.2.2 Monte Carlo algorithms; 6.2.3 Variance reduction; 6.2.4 The Brownian Bridge; 6.2.5 Early…”
Full text (MFA users only)
Electronic eBook -
346
An introduction to optical wireless mobile communications
Published 2021Table of Contents: “…-- 4.6 Summary -- References -- 5 Optical Wireless Communication Channel -- 5.1 Introduction -- 5.2 Channel Effects and Metrics -- 5.2.1 Channel Effects -- 5.2.2 Channel Metrics -- 5.2.3 Channel Decomposition -- 5.3 Front-End Channel -- 5.3.1 Exponential Function Approximation -- 5.3.2 First-Order Butterworth Filter Model -- 5.3.3 White LED Model -- 5.4 Optical Wireless Channel -- 5.4.1 LOS Channel -- 5.4.2 NLOS Channel -- 5.4.3 K-Factor -- 5.5 Deterministic NLOS Channel Simulation Approaches -- 5.5.1 Recursive Algorithm -- 5.5.2 Iterative Algorithm -- 5.5.3 DUSTIN Algorithm -- 5.5.4 Frequency-Domain Algorithm -- 5.5.5 Performance Evaluation -- 5.6 Monte Carlo NLOS Channel Simulation Approaches -- 5.6.1 Photon-Tracing Algorithm -- 5.6.2 Monte Carlo Ray-Shooting and Ray-Gathering Algorithms -- 5.6.3 Markov Chain Monte Carlo Algorithms -- 5.6.4 Performance Evaluation -- 5.7 Analytical NLOS Channel Modeling -- 5.7.1 Sphere-Integrating/Exponential-Decaying Model -- 5.7.2 Ceiling Bounce Model -- 5.7.3 Efficient Analytical Calculation Method -- 5.7.4 Performance Evaluation -- 5.8 Simulation Approach/Modeling Comparison -- 5.9 OWC Channel Characteristics.…”
Full text (MFA users only)
Electronic eBook -
347
Advanced Control and Optimization Paradigms for Energy System Operation and Management.
Published 2023Full text (MFA users only)
Electronic eBook -
348
Applied Predictive Analytics : Principles and Techniques for the Professional Data Analyst
Published 2014Full text (MFA users only)
Electronic eBook -
349
Language, vision, and music : selected papers from the 8th International Workshop on the Cognitive Science of Natural Language Processing, Galway, Ireland, 1999
Published 2002Table of Contents: “…-- The analogical foundations of creativity in language, culture & -- the arts: "The Upper Paleolithic to 2100CE" -- Creativity in humans, computers, and the rest of God's creatures. …”
Full text (MFA users only)
Electronic Conference Proceeding eBook -
350
-
351
-
352
-
353
Short Stories and Political Philosophy : Power, Prose, and Persuasion.
Published 2018Table of Contents: “…Social Interactions, Observation, and JusticeHacking the Human Condition: Enter Big Data; Digital Natives; Surveillance and the Human Condition; The Economics of Big Data; Increased Data, Better Algorithms, More Perfect Matches; Notes; Bibliography; Chapter 3; Paolo Bacigalupi's "Pop Squad" and the Examined Life Worth Living; The Symposium and the Search for Immortality; Children of the Body; Children of the Soul; Meaningless Life; Notes; Bibliography; Chapter 4; All the World's a Cage; Meaning and the Masses; Soul Meets Body; The End of Art; Conclusion; Notes; Bibliography; Chapter 5…”
Full text (MFA users only)
Electronic eBook -
354
-
355
-
356
Mechanisms and games for dynamic spectrum allocation
Published 2013Full text (MFA users only)
Electronic eBook -
357
A Primer on Machine Learning Applications in Civil Engineering
Published 2019Table of Contents: “…Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- Acknowledgments -- A Primer on Machine Learning Applications in Civil Engineering -- Author -- 1: Introduction -- 1.1 Machine Learning -- 1.2 Learning from Data -- 1.3 Research in Machine Learning: Recent Progress -- 1.4 Artificial Neural Networks -- 1.5 Fuzzy Logic (FL) -- 1.6 Genetic Algorithms -- 1.7 Support Vector Machine (SVM) -- 1.8 Hybrid Approach (HA) -- Bibliography -- 2: Artificial Neural Networks -- 2.1 Introduction to Fundamental Concepts and Terminologies -- 2.2 Evolution of Neural Networks -- 2.3 Models of ANN -- 2.4 McCulloch-Pitts Model -- 2.5 Hebb Network -- 2.6 Summary -- 2.7 Supervised Learning Network -- 2.7.1 Perceptron Network -- 2.7.2 Adaptive Linear Neuron -- 2.7.3 Back-Propagation Network -- 2.7.4 Radial Basis Function Network -- 2.7.5 Generalized Regression Neural Networks -- 2.7.6 Summary -- 2.8 Unsupervised Learning Networks -- 2.8.1 Introduction -- 2.8.2 Kohonen Self-Organizing Feature Maps -- 2.8.3 Counter Propagation Network -- 2.8.4 Adaptive Resonance Theory Network -- 2.8.5 Summary -- 2.9 Special Networks -- 2.9.1 Introduction -- 2.9.2 Gaussian Machine -- 2.9.3 Cauchy Machine -- 2.9.4 Probabilistic Neural Network -- 2.9.5 Cascade Correlation Neural Network -- 2.9.6 Cognitive Network -- 2.9.7 Cellular Neural Network -- 2.9.8 Optical Neural Network -- 2.9.9 Summary -- 2.10 Working Principle of ANN -- 2.10.1 Introduction -- 2.10.2 Types of Activation Function -- 2.10.3 ANN Architecture -- 2.10.4 Learning Process -- 2.10.5 Feed-Forward Back Propagation -- 2.10.6 Strengths of ANN -- 2.10.7 Weaknesses of ANN -- 2.10.8 Working of the Network -- 2.10.9 Summary -- Bibliography -- 3: Fuzzy Logic -- 3.1 Introduction to Classical Sets and Fuzzy Sets -- 3.1.1 Classical Sets -- 3.1.2 Fuzzy Sets -- 3.1.3 Summary.…”
Full text (MFA users only)
Electronic eBook -
358
Microwave and millimeter wave circuits and systems : emerging design, technologies, and applications
Published 2012Table of Contents: “…1.1.7 MBF Model -- the Memoryless PA Behavioural Model of ChoiceAcknowledgements; References; 2 Artificial Neural Network in Microwave Cavity Filter Tuning; 2.1 Introduction; 2.2 Artificial Neural Networks Filter Tuning; 2.2.1 The Inverse Model of the Filter; 2.2.2 Sequential Method; 2.2.3 Parallel Method; 2.2.4 Discussion on the ANN's Input Data; 2.3 Practical Implementation -- Tuning Experiments; 2.3.1 Sequential Method; 2.3.2 Parallel Method; 2.4 Influence of the Filter Characteristic Domain on Algorithm Efficiency; 2.5 Robots in the Microwave Filter Tuning; 2.6 Conclusions; Acknowledgement…”
Full text (MFA users only)
Electronic eBook -
359
-
360