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Автор: Grafiati
Опубліковано: 10 грудня 2022
Оновлено: 28 січня 2023

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Статті в журналах з теми "Modeling algorithms"

1

Barbulescu, L., A. E. Howe, L. D. Whitley, and M. Roberts. "Understanding Algorithm Performance on an Oversubscribed Scheduling Application." Journal of Artificial Intelligence Research 27 (December 28, 2006): 577–615. http://dx.doi.org/10.1613/jair.2038.

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Анотація:
The best performing algorithms for a particular oversubscribed scheduling application, Air Force Satellite Control Network (AFSCN) scheduling, appear to have little in common. Yet, through careful experimentation and modeling of performance in real problem instances, we can relate characteristics of the best algorithms to characteristics of the application. In particular, we find that plateaus dominate the search spaces (thus favoring algorithms that make larger changes to solutions) and that some randomization in exploration is critical to good performance (due to the lack of gradient information on the plateaus). Based on our explanations of algorithm performance, we develop a new algorithm that combines characteristics of the best performers; the new algorithm's performance is better than the previous best. We show how hypothesis driven experimentation and search modeling can both explain algorithm performance and motivate the design of a new algorithm.
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2

Agapie, Alexandru. "Theoretical Analysis of Mutation-Adaptive Evolutionary Algorithms." Evolutionary Computation 9, no. 2 (June 2001): 127–46. http://dx.doi.org/10.1162/106365601750190370.

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Adaptive evolutionary algorithms require a more sophisticated modeling than their static-parameter counterparts. Taking into account the current population is not enough when implementing parameter-adaptation rules based on success rates (evolution strategies) or on premature convergence (genetic algorithms). Instead of Markov chains, we use random systems with complete connections - accounting for a complete, rather than recent, history of the algorithm's evolution. Under the new paradigm, we analyze the convergence of several mutation-adaptive algorithms: a binary genetic algorithm, the 1/5 success rule evolution strategy, a continuous, respectively a dynamic (1+1) evolutionary algorithm.
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3

Merkle, Daniel, and Martin Middendorf. "Modeling the Dynamics of Ant Colony Optimization." Evolutionary Computation 10, no. 3 (September 2002): 235–62. http://dx.doi.org/10.1162/106365602760234090.

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Анотація:
The dynamics of Ant Colony Optimization (ACO) algorithms is studied using a deterministic model that assumes an average expected behavior of the algorithms. The ACO optimization metaheuristic is an iterative approach, where in every iteration, artificial ants construct solutions randomly but guided by pheromone information stemming from former ants that found good solutions. The behavior of ACO algorithms and the ACO model are analyzed for certain types of permutation problems. It is shown analytically that the decisions of an ant are influenced in an intriguing way by the use of the pheromone information and the properties of the pheromone matrix. This explains why ACO algorithms can show a complex dynamic behavior even when there is only one ant per iteration and no competition occurs. The ACO model is used to describe the algorithm behavior as a combination of situations with different degrees of competition between the ants. This helps to better understand the dynamics of the algorithm when there are several ants per iteration as is always the case when using ACO algorithms for optimization. Simulations are done to compare the behavior of the ACO model with the ACO algorithm. Results show that the deterministic model describes essential features of the dynamics of ACO algorithms quite accurately, while other aspects of the algorithms behavior cannot be found in the model.
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4

Merheb, Abdel-Razzak, Hassan Noura, and François Bateman. "Mathematical Modeling of Ecological Systems Algorithm." Lebanese Science Journal 22, no. 2 (March 2, 2022): 209–31. http://dx.doi.org/10.22453/lsj-022.2.209-231.

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In this paper, the mathematical modeling of a new bio-inspired evolutionary search algorithm called Ecological Systems Algorithm (ESA) is presented. ESA imitates ecological rules to find iteratively the optimum of a given function through interaction between predator and prey search species. ESA is then compared to the well-known Genetic Algorithm which is a powerful bio-inspired stochastic search/optimization algorithm used for decades. Simulation results of the two algorithms optimizing ten different benchmark functions are used to investigate and compare both algorithms based on their speed, performance, reliability, and efficiency.
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5

Beattie, Ian D. "Modeling Operations and Algorithms." Arithmetic Teacher 33, no. 6 (February 1986): 23–28. http://dx.doi.org/10.5951/at.33.6.0023.

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The idea of using manipulative materials to model operation and algorithms is not new. The Arithmetic Teacher frequently includes articles on the topic, and its readers should have accumulated a wealth of excellent teaching suggestions. Current methodology books and school texts place great emphasis on the use of manipulatives. Thus one might expect appropriate manipulative materials to be a mainstay of every mathematics classroom, but evidence suggests otherwise. Although teachers believe that manipulative materials should be used for mathematics instruction and that the usc of such materials does develop understanding, the use of manipulative materials diminishes through the grade (Suydam 1984a).
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6

Vose, Michael D. "Modeling Simple Genetic Algorithms." Evolutionary Computation 3, no. 4 (December 1995): 453–72. http://dx.doi.org/10.1162/evco.1995.3.4.453.

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Анотація:
The infinite- and finite-population models of the simple genetic algorithm are extended and unified, The result incorporates both transient and asymptotic GA behavior. This leads to an interpretation of genetic search that partially explains population trajectories. In particular, the asymptotic behavior of the large-population simple genetic algorithm is analyzed.
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7

Li, Jing Zhu, Qian Li, Tai Yu Liu, and Wei Hong Niu. "Data Mining: Modeling, Algorithms, Applications and Systems." Advanced Materials Research 926-930 (May 2014): 2786–89. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.2786.

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Анотація:
Data mining is a multidisciplinary field of the 20th century gradually, this paper based on data mining modeling, algorithms, applications and software tools were reviewed, the definition of data mining, the scope and characteristics of the data sets and data mining various practical situations; summarizes the data mining in the practical application of the basic steps and processes; data mining tasks in a variety of applications and modeling issues were discussed; cited the current field of data mining is mainly popular algorithms, and algorithm design issues to consider briefly analyzed; overview of the current data mining algorithm in a number of areas; more comprehensive description of the current performance and data mining software tools developer circumstances; Finally, the development of data mining prospects and direction prospected.
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8

STEWART, A. JAMES. "LOCAL ROBUSTNESS AND ITS APPLICATION TO POLYHEDRAL INTERSECTION." International Journal of Computational Geometry & Applications 04, no. 01 (March 1994): 87–118. http://dx.doi.org/10.1142/s0218195994000070.

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The field of solid modeling makes extensive ve use of a variety of geometric algorithms. When implemented on a computer, these algorithms often fail because the computer only provides floating point arithmetic, while the algorithms are expecting infinite precision arithmetic on real numbers. These algorithms are not robust. This paper presents a formal theory of robustness. It is then argued that the elegant theoretical approach to robustness is not viable in practice: algorithms like those used in solid modeling are generally too complex for this approach. This paper presents a practical alternative to the formal theory of robustness; this alternative is called local robustness. Local robustness is applied to the design of a polyhedral intersection algorithm, which is an important component in many solid modelers. The intersection algorithm has been implemented, and, in extensive tests, has never failed to produce a valid polyhedron of intersection. A concise characterization of the locally robust intersection algorithm is presented; this characterization can be used to develop variants of the intersection algorithm, and to develop robust versions of other solid modeling algorithms.
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9

Wang, Yang, Shi Jun Ji, and Li Jun Yang. "General Subdivision Inferred from Catmull-Clark Subdivision Algorithm." Materials Science Forum 532-533 (December 2006): 789–92. http://dx.doi.org/10.4028/www.scientific.net/msf.532-533.789.

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Subdivision algorithms have emerged recently as a powerful and useful technique in modeling free-form surfaces. Subdivision algorithms exited at present however, being their disadvantages, can’t meet the demand of wide application in modeling surfaces and don’t still belong to a general theory. In this paper, a general subdivision algorithm is presented which is a general conclusion inferred from classical Catmull-Clark subdivision algorithm and can produce existing subdivision algorithm by selecting reasonable vertical weights and horizontal weights. The subdivision algorithm is an ideal resolution for keeping shape feature such as crease, corner and dart contrast to all existing subdivision algorithms, it also have the advantage of flexible weights selection, easily control of shape and high compute speed. Therefore, the algorithms are extensively applicable for shape modeling in computer aided geometric design, industrial prototype design and reverse engineering.
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10

Potapov, Viktor S., and Sergei M. Gushansky. "Development of a technique for modeling entangled quantum computations that are applicable in Simon’s quantum algorithm." Informatization and communication, no. 3 (May 5, 2020): 66–70. http://dx.doi.org/10.34219/2078-8320-2020-11-3-66-70.

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This paper describes the basics of developing quantum algorithms and modeling entangled quantum computations applicable in quantum algorithms. Quantum algorithms involve the use of vector and matrix algebra. The basic tasks of the simulation proposed in the work are determined within the framework of the algorithm for executing quantum algorithms, taking into account entanglement. A technique has been developed for modeling entangled quantum calculations applicable in the Simon quantum algorithm, which helps to predict the behavior of the quantum algorithm (or any other computing process that proceeds as part of the work of a quantum computer system) with partial entanglement.
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Дисертації з теми "Modeling algorithms"

1

Frank, Matthew I. "LoPC-- modeling contention in parallel algorithms." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/47439.

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2

DeBrunner, Linda Sumners. "Modeling reconfiguration algorithms for regular architecture." Diss., Virginia Tech, 1991. http://hdl.handle.net/10919/29254.

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Анотація:
Three models are proposed to evaluate and design distributed reconfigurable systems for fault tolerant, highly reliable applications. These models serve as valuable tools for developing fault tolerant systems. In each model, cells work together in parallel to change the global structure through a series of separate actions. In the Local Supervisor Model (LSM), selected cells guide the reconfiguration process. In the Tessellation Automata Model (TAM), each cell determines its next state based on its state and its neighbors' states, and communicates its state information to its neighbors. In the Interconnected Finite State Machine Model (IFS:MM:), each cell determines its next state and outputs based on its state and its inputs. The hierarchical nature of the TAM and IFSMM provides advantages in evaluating, comparing, and designing systems. The use of each of these models in describing systems is demonstrated. The IFSMM: is emphasized since it is the most versatile of the three models. The IFSMM: is used to identify algorithm weaknesses and improvements, compare existing algorithms, and develop a novel design for a reconfigurable hypercube.
Ph. D.
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3

Zhang, Wangyang. "IC Spatial Variation Modeling: Algorithms and Applications." Research Showcase @ CMU, 2012. http://repository.cmu.edu/dissertations/136.

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Анотація:
Rapidly improving the yield of today's complicated manufacturing process is a key challenge to ensure profitability for the IC industry. In this thesis, we propose accurate and efficient modeling techniques for spatial variation, which is becoming increasing important in the advanced technology nodes. Based on the spatial model, we develop algorithms for two applications that help identify the important yield-limiting factors and prioritize yield improvement efforts. Variation decomposition narrows down the sources of variation by decomposing the overall variation into multiple different components, each corresponding to a different subset of variation sources. Wafer spatial signature clustering automatically partitions a large number of wafers into groups exhibiting different spatial signatures, which helps process engineers find important factors that prevent the process from stably maintaining a high yield across different lots and wafers. An important problem in variation decomposition is to accurately model and extract the wafer-level and within-die spatially correlated variation. Towards this goal, we first develop a physical basis function dictionary based on our study of several common physical variation sources. We further propose the DCT dictionary to discover spatially correlated systematic patterns not modeled by the physical dictionary. Moreover, we propose to apply sparse regression to significantly reduce the over-fitting problem posed by a large basis function dictionary. We further extend the sparse regression algorithm to a robust sparse regression algorithm for outlier detection, which provides superior accuracy compared to the traditional IQR method. Finally, we propose several efficient methods to make the computational cost of sparse regression tractable for large-scale problems. We further develop an algorithm for the wafer spatial signature clustering problem based on three steps. First, we re-use the spatial variation modeling technique developed for variation decomposition to automatically capture the spatial signatures of wafers by a small number of features. Next, we select a complete-link hierarchical clustering algorithm to perform clustering on the features. Finally, we develop a modified L-method to select the number of clusters from the hierarchical clustering result.
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4

Yee, Seung Hee. "Three algorithms for planar-patch terrain modeling." Thesis, Monterey, California. Naval Postgraduate School, 1988. http://hdl.handle.net/10945/23136.

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Providing a simplified model of real terrain has applications to route planning for robotic vehicles and military maneuvers. In this thesis I explore planar-patch surface modeling to represent terrain in a simple and effective way. In planar-patch surface modeling the terrain is subdivided into a set of planar subregions. The homogeneity of the gradient within a planar subregion simplifies calculating the cost of traversing the region, thus simplifying route planning. I have explored three main strategies to model the surface: joint top-down and and bottom-up, strict bottom-up, and presmoothing bottom-up approaches. Results of the algorithms are shown graphically by using the APL and Grafstat packages, verifying their correctness and accuracy.
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5

Chen, Lin. "Causal modeling in quantitative genomics /." Thesis, Connect to this title online; UW restricted, 2008. http://hdl.handle.net/1773/9577.

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6

Bosson, Maël. "Adaptive algorithms for computational chemistry and interactive modeling." Phd thesis, Université de Grenoble, 2012. http://tel.archives-ouvertes.fr/tel-00846458.

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At the atomic scale, interactive physically-based modeling tools are more and more in demand. Unfortunately, solving the underlying physics equations at interactive rates is computationally challenging. In this dissertation, we propose new algorithms that allow for interactive modeling of chemical structures. We first present a modeling tool to construct structural models of hydrocarbon systems. The physically-based feedbacks are based on the Brenner potential. In order to be able to interactively edit systems containing numerous atoms, we introduce a new adaptive simulation algorithm. Then, we introduce what we believe to be the first interactive quantum chemistry simulation algorithm at the Atom Superposition and Electron Delocalization Molecular Orbital (ASED-MO) level of theory. This method is based on the divide-and-conquer (D&C) approach, which we show is accurate and efficient for this non-self-consistent semi-empirical theory. We then propose a novel Block-Adaptive Quantum Mechanics (BAQM) approach to interactive quantum chemistry. BAQM constrains some nuclei positions and some electronic degrees of freedom on the fly to simplify the simulation. Finally, we demonstrate several applications, including one study of graphane formation, interactive simulation for education purposes, and virtual prototyping at the atomic scale, both on desktop computers and in virtual reality environments.
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7

Lam, Warren Michael. "Modeling algorithms for a class of fractal signals." Thesis, Massachusetts Institute of Technology, 1992. http://hdl.handle.net/1721.1/31034.

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Анотація:
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1992.
Includes bibliographical references (leaves 86-87).
by Warren Michael Lam.
M.S.
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8

Stuhlmüller, Andreas. "Modeling cognition with probabilistic programs : representations and algorithms." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/100860.

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Анотація:
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, 2015.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 167-176).
This thesis develops probabilistic programming as a productive metaphor for understanding cognition, both with respect to mental representations and the manipulation of such representations. In the first half of the thesis, I demonstrate the representational power of probabilistic programs in the domains of concept learning and social reasoning. I provide examples of richly structured concepts, defined in terms of systems of relations, subparts, and recursive embeddings, that are naturally expressed as programs and show initial experimental evidence that they match human generalization patterns. I then proceed to models of reasoning about reasoning, a domain where the expressive power of probabilistic programs is necessary to formalize our intuitive domain understanding due to the fact that, unlike previous formalisms, probabilistic programs allow conditioning to be represented in a model, not just applied to a model. I illustrate this insight with programs that model nested reasoning in game theory, artificial intelligence, and linguistics. In the second half, I develop three inference algorithms with the dual intent of showing how to efficiently compute the marginal distributions defined by probabilistic programs, and providing building blocks for process-level accounts of human cognition. First, I describe a Dynamic Programming algorithm for computing the marginal distribution of discrete probabilistic programs by compiling to systems of equations and show that it can make inference in models of "reasoning about reasoning" tractable by merging and reusing subcomputations. Second, I introduce the setting of amortized inference and show how learning inverse models lets us leverage samples generated by other inference algorithms to compile probabilistic models into fast recognition functions. Third, I develop a generic approach to coarse-to-fine inference in probabilistic programs and provide evidence that it can speed up inference in models with large state spaces that have appropriate hierarchical structure. Finally, I substantiate the claim that probabilistic programming is a productive metaphor by outlining new research questions that have been opened up by this line of investigation.
by Andreas Stuhlmüller.
Ph. D.
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9

Chaudhari, Soumee. "Modeling distance functions induced by face recognition algorithms." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000516.

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10

Hedberg, Vilhelm. "Evaluation of Hair Modeling, Simulation and Rendering Algorithms for a VFX Hair Modeling System." Thesis, Linköpings universitet, Medie- och Informationsteknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-65592.

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Creating realistic virtual hair consists of several major areas: creating the geometry, moving the hair strands realistically and rendering the hair. In this thesis, a background survey covering each one of these areas is given. A node-based, procedural hair system is presented, which utilizes the capabilities of modern GPUs. The hair system is implemented as a plugin for Autodesk Maya, and a user interface is developed to allow the user to control the various parameters. A number of nodes are developed to create effects such as clumping, noise and frizz. The proposed system can easily handle a variety of hairstyles, and pre-renders the result in real-time using a local shading model.
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Книги з теми "Modeling algorithms"

1

Wilson, Caroline L. Clustering algorithms and mathematical modeling. Hauppauge, N.Y: Nova Science Publishers, 2010.

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2

Agnarsson, Geir. Graph theory: Modeling, applications, and algorithms. Upper Saddle River, N.J: Pearson/Prentice Hall, 2007.

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3

George, Betsy. Spatio-temporal Networks: Modeling and Algorithms. New York, NY: Springer New York, 2013.

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4

E, Farin Gerald, Society for Industrial and Applied Mathematics., and SIAM Conference on Geometric Modeling and Robotics (1985 : Albany, N.Y.), eds. Geometric modeling: Algorithms and new trends. Philadelphia, Pa: Society for Industrial and Applied Mathematics, 1987.

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5

Grigoʹevich, Ivakhnenko Alekseĭ, ed. Inductive learning algorithms for complex systems modeling. Boca Raton: CRC Press, 1994.

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6

Cai, Yang. Digital human modeling: Trends in human algorithms. Berlin: Springer, 2008.

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7

Cai, Yang. Digital human modeling: Trends in human algorithms. Berlin: Springer, 2008.

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8

Yee, Seung Hee. Three algorithms for planar-patch terrain modeling. Monterey, Calif: Naval Postgraduate School, 1988.

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9

Digital human modeling: Trends in human algorithms. Berlin: Springer, 2008.

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10

Mariño, Pablo Pavón. Optimization of Computer Networks - Modeling and Algorithms. Chichester, UK: John Wiley & Sons, Ltd, 2016. http://dx.doi.org/10.1002/9781119114840.

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Частини книг з теми "Modeling algorithms"

1

Suthaharan, Shan. "Modeling and Algorithms." In Machine Learning Models and Algorithms for Big Data Classification, 123–43. Boston, MA: Springer US, 2016. http://dx.doi.org/10.1007/978-1-4899-7641-3_6.

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2

Leenaerts, Domine M. W., and Wim M. G. Van Bokhoven. "Solution Algorithms." In Piecewise Linear Modeling and Analysis, 79–104. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4757-6190-0_3.

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3

Kulkarni, Akshay R., Adarsha Shivananda, Anoosh Kulkarni, and V. Adithya Krishnan. "Statistical Univariate Modeling." In Time Series Algorithms Recipes, 33–66. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8978-5_2.

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4

Babuška, Robert. "Fuzzy Clustering Algorithms." In Fuzzy Modeling for Control, 49–74. Dordrecht: Springer Netherlands, 1998. http://dx.doi.org/10.1007/978-94-011-4868-9_3.

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5

Martens, Luc. "Measurement-Based Modeling Algorithms." In Electronic Packaging and Interconnects Series, 97–154. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5623-7_5.

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6

Boyadjiev, Christo, Maria Doichinova, Boyan Boyadjiev, and Petya Popova-Krumova. "Multi-step Modeling Algorithms." In Modeling of Column Apparatus Processes, 259–70. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-89966-4_9.

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7

Boyadjiev, Christo, Maria Doichinova, Boyan Boyadjiev, and Petya Popova-Krumova. "Multi-step Modeling Algorithms." In Modeling of Column Apparatus Processes, 255–66. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28259-6_9.

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8

Spellman, Frank R. "Environmental Modeling and Algorithms." In Handbook of Environmental Engineering, 113–30. 2nd ed. New York: CRC Press, 2023. http://dx.doi.org/10.1201/9781003298601-4.

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9

Bergman, David, Andre A. Cire, Willem-Jan van Hoeve, and John Hooker. "Recursive Modeling." In Artificial Intelligence: Foundations, Theory, and Algorithms, 137–56. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42849-9_8.

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10

Gao, Yuehua, Peng Zhao, Lih-Sheng Turng, and Huamin Zhou. "Intelligent Optimization Algorithms." In Computer Modeling for Injection Molding, 283–92. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118444887.ch10.

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Тези доповідей конференцій з теми "Modeling algorithms"

1

Schmidt, David P., and Christopher J. Rutland. "Reducing Grid Dependency in Droplet Collision Modeling." In ASME 2001 Internal Combustion Engine Division Fall Technical Conference. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/2001-ice-395.

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Abstract A faster, more accurate replacement for existing collision algorithms has been developed. The method, called the NTC algorithm, is not grid dependent, and is much faster than older algorithms. Calculations with sixty thousand parcels required only a few CPU minutes. However, there is a significant need to develop mesh-independent momentum coupling between the gas and spray, so that the collision algorithm’s full accuracy can be fully realized.
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2

"MODELING INTERNAL RADIATION THERAPY." In International Conference on Bioinformatics Models, Methods and Algorithms. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003172202280233.

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3

He, Li, Qi Meng, Wei Chen, Zhi-Ming Ma, and Tie-Yan Liu. "Differential Equations for Modeling Asynchronous Algorithms." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/307.

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Asynchronous stochastic gradient descent (ASGD) is a popular parallel optimization algorithm in machine learning. Most theoretical analysis on ASGD take a discrete view and prove upper bounds for their convergence rates. However, the discrete view has its intrinsic limitations: there is no characterizationof the optimization path and the proof techniques are induction-based and thus usually complicated. Inspired by the recent successful adoptions of stochastic differential equations (SDE) to the theoretical analysis of SGD, in this paper, we study the continuous approximation of ASGD by using stochastic differential delay equations (SDDE). We introduce the approximation method and study the approximation error. Then we conduct theoretical analysis on the convergence rate of ASGD algorithm based on the continuous approximation.There are two methods: moment estimation and energy function minimization can be used to analyzethe convergence rates. Moment estimation depends on the specific form of the loss function, while energy function minimization only leverages the convex property of the loss function, and does not depend on its specific form. In addition to the convergence analysis, the continuous view also helps us derive better convergence rates. All of this clearly shows the advantage of taking the continuous view in gradient descent algorithms.
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Shamin, Evgeniy, Dmitriy Zhevnenko, Fedor Meshchaninov, Vladislav Kozhevnikov, and Evgeniy Gornev. "DETERMINATION OF THE INITIAL GUESS FOR THE PROBLEM OF MEMRISTOR MODEL PARAMETERS EXTRACTION USING MACHINE LEARNING ALGORITHMS." In Mathematical modeling in materials science of electronic component. LLC MAKS Press, 2020. http://dx.doi.org/10.29003/m1539.mmmsec-2020/127-130.

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The focus of this work is on the algorithm of extraction of parameters of the memristor model from the experimentally obtained current-voltage characteristics. The problem of finding the initial guess for this algorithm based on current-voltage characteristic features is stated and solved by means of machine learning algorithms.
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5

Reiner, Jacob. "Generic Development Environment for INS Algorithms." In Modeling and Simulation Technologies Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2000. http://dx.doi.org/10.2514/6.2000-4186.

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KAO, BEN, and MINGHUA ZHANG. "ALGORITHMS FOR MINING FREQUENT SEQUENCES." In Advances in Data Mining and Modeling. WORLD SCIENTIFIC, 2003. http://dx.doi.org/10.1142/9789812704955_0001.

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7

Chaturantabut, Saifon. "Nonlinear reduced-order modeling with monotonicity property." In NUMERICAL COMPUTATIONS: THEORY AND ALGORITHMS (NUMTA–2016): Proceedings of the 2nd International Conference “Numerical Computations: Theory and Algorithms”. Author(s), 2016. http://dx.doi.org/10.1063/1.4965383.

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Martyanov, A. S., D. V. Korobatov, and E. A. Sirotkin. "Modeling of battery charging algorithms." In 2016 2nd International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM). IEEE, 2016. http://dx.doi.org/10.1109/icieam.2016.7911469.

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Krömer, Pavel, Jan Platoš, and Václav Snášel. "Modeling Permutations for Genetic Algorithms." In 2009 International Conference of Soft Computing and Pattern Recognition. IEEE, 2009. http://dx.doi.org/10.1109/socpar.2009.31.

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Singh, Jaswinder Pal, Edward Rothberg, and Anoop Gupta. "Modeling communication in parallel algorithms." In the sixth annual ACM symposium. New York, New York, USA: ACM Press, 1994. http://dx.doi.org/10.1145/181014.181329.

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Звіти організацій з теми "Modeling algorithms"

1

Beattie, Christopher A., Jeffrey T. Borggaard, Serkan Gugercin, and Traian Iliescu. High Performance Parallel Algorithms for Improved Reduced-Order Modeling. Fort Belvoir, VA: Defense Technical Information Center, May 2008. http://dx.doi.org/10.21236/ada483934.

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2

Fringer, Oliver B. Development of Improved Algorithms and Multiscale Modeling Capability with SUNTANS. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada590685.

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3

Fringer, Oliver B. Development of Improved Algorithms and Multiscale Modeling Capability with SUNTANS. Fort Belvoir, VA: Defense Technical Information Center, September 2013. http://dx.doi.org/10.21236/ada601191.

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4

Fringer, Oliver B. Development of Improved Algorithms and Multiscale Modeling Capability with SUNTANS. Fort Belvoir, VA: Defense Technical Information Center, September 2011. http://dx.doi.org/10.21236/ada557106.

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5

Littlefield, R. J. Modeling node bandwidth limits and their effects on vector combining algorithms. Office of Scientific and Technical Information (OSTI), January 1992. http://dx.doi.org/10.2172/5727485.

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Littlefield, R. J. Modeling node bandwidth limits and their effects on vector combining algorithms. Office of Scientific and Technical Information (OSTI), January 1992. http://dx.doi.org/10.2172/10133355.

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7

Balanis, Constantine A., and Mark Frank. Modeling and Reconstruction Algorithms for Detection, Location, and Identification of Subsurface Anomalies. Fort Belvoir, VA: Defense Technical Information Center, June 1990. http://dx.doi.org/10.21236/ada224358.

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8

Pirohov, Vladyslav M., Anna M. Horlo, and Iryna S. Mintii. Software development of the algorithm of adaptating of the website design for people with color-blindness. [б. в.], December 2018. http://dx.doi.org/10.31812/123456789/2888.

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The purpose of the study is the program implementation of the basic algorithms of the website design adaptation for people with color perception. This article examines the implementation of the following algorithms: the conversion algorithm from the rgb color model to the lms model (there is a special algorithm precisely in the color model lms for modeling various types of color perception violations), simulation of different color blindness types in the lms-model (this is the transformation of normal values in the color model lms to values with different types of color perception violations), convert data from lms color model to rgb model, conversion from color rgb to hsl model, color filtering in hsl-model and conversion from hsl model to rgb color model.
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9

Liao, Wen-Jiao. Physics-Based Radiometric Signature Modeling and Detection Algorithms of Landmines Using Electro-Optical Sensors. Fort Belvoir, VA: Defense Technical Information Center, July 2005. http://dx.doi.org/10.21236/ada443784.

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10

Leimkuhler, B., J. Hermans, and R. D. Skeel. Workshop on algorithms for macromolecular modeling. Final project report, June 1, 1994--May 31, 1995. Office of Scientific and Technical Information (OSTI), July 1995. http://dx.doi.org/10.2172/88630.

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