Relevant bibliographies by topics / The hierarchical model

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'The hierarchical model'

Author: Grafiati
Published: 30 June 2021
Last updated: 5 May 2022

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Journal articles on the topic "The hierarchical model"

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Sheng-Guo Wang, Sheng-Guo Wang, Yong-Gang Liu Sheng-Guo Wang, and Tian-Wei Bai Yong-Gang Liu. "Dynamic Node Link Model of Hierarchical Edge Computing." 電腦學刊 32, no. 5 (October 2021): 222–32. http://dx.doi.org/10.53106/199115992021103205019.

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With the rise of the Internet of Things, edge computing has become one of the key technologies in Internet of Things solutions. In the context of the Industrial Internet of Things, hierarchical edge computing shows its advantages. This article focuses on hierarchical edge computing in the industrial Internet of Things scene, and studies the dynamic resource allocation of hierarchical edge computing networks. When using a hierarchical edge computing network with existing equipment, it is difficult to make changes to existing equipment. Therefore, this article uses queuing theory modeling analysis and proposes Dynamic Link Model based on Nodes Relation. Aiming at the hierarchical edge computing network, this model uses a method based on node connection relationship transfer to achieve load balancing of task flow and completes the dynamic allocation of computing resources in the network, and proposes a time experienced priority queue offloading strategy. The paper uses Java to achieve a dynamic link model experiment based on the connection relationship between nodes. The results show that this scheme has significant advantages in the global average delay of the system, and ensure the loss probability is reasonable within a certain limit.
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Zhi-Bo Wang, Zhi-Bo Wang. "Node Resource Management Model of Hierarchical Edge Computing." 電腦學刊 32, no. 5 (October 2021): 233–44. http://dx.doi.org/10.53106/199115992021103205020.

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This article focuses on hierarchical edge computing in the industrial Internet of Things scenario, and studies the static resource deployment of hierarchical edge computing networks. When deploying a hierarchical edge computing network with new equipment, the allocation of computing capacity between layers is one of the hot is-sues. This paper proposes a method for the allocation of edge computing node capacity between layers based on the M/M/1/c queue model, delay and call loss are performance indicators, and the optimal inter-layer capacity allocation algorithm is designed and implemented. This algorithm can reduce the global average delay of the sys-tem under the premise of meeting the requirement of call loss rate. Simulation verification shows that the optimal inter-layer capacity allocation algorithm can effectively reduce the system’s global average delay and call loss rate under the condition of a certain total system cost.
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Tashiro, Tohru. "Hierarchical Bass model." Journal of Physics: Conference Series 490 (March 11, 2014): 012181. http://dx.doi.org/10.1088/1742-6596/490/1/012181.

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SONG, CHEE-YANG, and DOO-KWON BAIK. "A LAYERED METAMODEL FOR HIERARCHICAL MODELING IN UML." International Journal of Software Engineering and Knowledge Engineering 13, no. 02 (April 2003): 191–214. http://dx.doi.org/10.1142/s0218194003001263.

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As software is becoming larger and more complex, it is increasingly important to use the hierarchical modeling approach. Unfortunately, however, UML does not specify each metamodel with hierarchy for model by modeling phase. Thus, most UML-based methodologies do not address the hierarchical modeling for model. As a method for supporting hierarchical modeling on UML, this paper proposes a layered metamodel which defines hierarchically modeling elements of model according to the modeling phase. We describe each metamodel with hierarchy for models in UML, then present the hierarchical integrated metamodel combined with each metamodel by three modeling phases (conceptual phase, specific phase, and concrete phase). Therefore, designers are able to construct the hierarchical model by applying the metamodel with hierarchy. Using the hierarchical metamodel enables designers to improve the usability of UML and reusability of application model.
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Aly, S., and I. Vrana. "Multiple parallel fuzzy expert systems utilizing a hierarchical fuzz model." Agricultural Economics (Zemědělská ekonomika) 53, No. 2 (January 7, 2008): 89–93. http://dx.doi.org/10.17221/1425-agricecon.

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Business, economic, and agricultural YES-or-NO decision making problems often require multiple, different and specific expertises. This is due to the nature of such problems in which decisions may be influenced by multiple different, relevant aspects, and accordingly multiple corresponding expertises are required. Fuzzy expert systems (FESs) are widely used to model expertises due to its capability to model real world values, which are not always exact, but frequently vague or uncertain. In this research, different expertises, relevant to the decision solution, are modeled using several corresponding FESs. Every FES produces a crisp numerical output expressing the degree of bias toward “Yes” or “No“ decision. A unified scale is standardized for numerical outputs of all FESs. This scale ranges from 0 to 10, where the value 0 represents a complete bias ”No“ decision and the value 10 represents a complete bias to ”Yes“ decision. Intermediate values reflect the degree of bias either to ”Yes“ or ”No“ decision. These systems are then integrated to comprehensibly judge the binary decision problem, which requires all such expertises. Practically, the main reasons for independency among the multiple FESs can be related to maintainability, decision responsibility, analyzability, knowledge cohesion and modularity, context flexibility, sensitivity of aggregate knowledge, decision consistency, etc. The proposed mechanism for realizing integration is a hierarchical fuzzy system (HFS) based model, which allows the utilization of the existing If-then knowledge about how to combine/aggregate the outputs of FESs.
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Ndung’u, A. W., S. Mwalili, and L. Odongo. "Hierarchical Penalized Mixed Model." Open Journal of Statistics 09, no. 06 (2019): 657–63. http://dx.doi.org/10.4236/ojs.2019.96042.

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Mozetič, Igor. "Hierarchical model-based diagnosis." International Journal of Man-Machine Studies 35, no. 3 (September 1991): 329–62. http://dx.doi.org/10.1016/s0020-7373(05)80132-4.

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Lin, Zhifang, and Ruibao Tao. "Hierarchical quantum Ising model." Physical Review B 41, no. 16 (June 1, 1990): 11597–99. http://dx.doi.org/10.1103/physrevb.41.11597.

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Paluch, R., K. Suchecki, and J. A. Hołyst. "Hierarchical Cont-Bouchaud Model." Acta Physica Polonica A 127, no. 3a (March 2015): A—108—A—112. http://dx.doi.org/10.12693/aphyspola.127.a-108.

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Lohrey, Markus. "Model-checking hierarchical structures." Journal of Computer and System Sciences 78, no. 2 (March 2012): 461–90. http://dx.doi.org/10.1016/j.jcss.2011.05.006.

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Dissertations / Theses on the topic "The hierarchical model"

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Kritchevski, Evgenij. "Hierarchical Anderson model." Thesis, McGill University, 2008. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=115890.

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In this thesis, we study the spectral properties of the hierarchical Anderson model. This model is an approximation of the Anderson tight-binding model on Zd , with the usual discrete Laplacian replaced by a hierarchical long-range interaction operator. In the hierarchical Anderson model, we are given a countable set X endowed with a hierarchical structure. The free hierarchical Laplacian is a self-adjoint operator Delta acting on the Hilbert space l 2( X ). The spectrum of Delta consists of isolated infinitely degenerate eigenvalues. We look at small random perturbations of the operator Delta. The disorder is modeled by a random potential Vo, (Vopsi)(x) = o( x)psi(x) for psi ∈ l 2( X ). The numbers o(x) are identically distributed independent random variables with a bounded density. The hierarchical Anderson model is the random self-adjoint operator Ho = Delta + Vo. We prove the following two results. If the model has a spectral dimension dsp ≤ 4 then, almost surely, the spectrum of Ho is dense pure-point. The second result is on eigenvalue statistics. For dsp < 1, the energy levels for Ho are asymptotically a Poisson point process in the thermodynamic limit, after a proper rescaling.
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Busatto, Giorgio. "An abstract model of hierarchical graphs and hierarchical graph transformation." Oldenburg : Univ., Fachbereich Informatik, 2002. http://deposit.ddb.de/cgi-bin/dokserv?idn=967851955.

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Sodhi, Manbir Singh. "An hierarchical model for FMS control." Diss., The University of Arizona, 1991. http://hdl.handle.net/10150/185364.

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Flexible Manufacturing Systems (FMSs) are usually composed of general purpose machines with automatic tool changing capability and integrated material handling. FMSs offer the advantages of high utilization levels and simultaneous production of a variety of part types with minimal changeover time. The complexity of FMSs however requires sophisticated control. In this dissertation a four level control hierarchy along with computationally feasible control algorithms for each level is presented. Decisions are made at each level utilizing the flexibility inherent in FMSs. The proposed scheme has the advantages of ensuring satisfaction of higher level decisions as lower level operating decisions are made, and allows performance and status data collected at lower levels to be fed back and influence future high level decisions. The top level is concerned with the choice of part types and volumes to be assigned to the FMS over the next several months. Within this horizon, production volumes are planned for each period, a period typically being between a week and a month in length. A linear programming model is used for planning at this level. The second level plans daily or shift production. Advantage is taken of the FMSs ability to be configured to respond to different part mixes to allocate tools to machines so as to minimize holding costs. Separate mathematical programming models are formulated to match various FMS environments. A heuristic for solution of a model of an automated production flexible environment is detailed. Computational results are presented. Extensions of this heuristic to other environments are outlined. The third level determines process routes for each part type in order to minimize material handling. Additional tools are loaded on machines when possible to maximize alternate routings, and using the flexibility offered by FMSs to process parts along alternate routes, routing assignments are made to minimize workload assignment. These routing assignments are used by level four for actual routing, sequencing and material handling path control. The level three model is formulated as a linear program and heuristics are used for level four. An example is provided to illustrate the completeness of the decision hierarchy and the relationships between levels.
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Blayneh, Kbenesh W. "A hierarchical size-structured population model." Diss., The University of Arizona, 1996. http://hdl.handle.net/10150/187505.

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A model is considered for the dynamics of a size-structured population in which the birth, death and growth rates of an individual of size s are functions of the total population biomass of all individuals of size larger or smaller than s. The dynamics of the size distribution is governed by the McKendrick equations. An existence/uniqueness theorem for this equation is proved using an equivalent pair of partial and ordinary differential equations. The asymptotic dynamics of the density function is studied and some applications of the model to intraspecific predation and certain types of intraspecific competitions are given.
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BEZERRA, ROSINI ANTONIO MONTEIRO. "HIERARCHICAL NEURO-FUZZY BSP-MAMDANI MODEL." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2002. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=3129@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
Esta dissertação investiga a utilização de sistemas Neuro- Fuzzy Hierárquicos BSP (Binary Space Partitioning) para aplicações em classificação de padrões, previsão, sistemas de controle e extração de regras fuzzy. O objetivo é criar um modelo Neuro-Fuzzy Hierárquico BSP do tipo Mamdani a partir do modelo Neuro-Fuzzy Hierárquico BSP Class (NFHB-Class) que é capaz de criar a sua própria estrutura automaticamente e extrair conhecimento de uma base de dados através de regras fuzzy, lingüisticamente interpretáveis, que explicam a estrutura dos dados. Esta dissertação consiste de quatros etapas principais: estudo dos principais sistemas hierárquicos; análise do sistema Neuro-Fuzzy Hierárquico BSP Class, definição e implementação do modelo NFHB-Mamdani e estudo de casos. No estudo dos principais sistemas hierárquicos é efetuado um levantamento bibliográfico na área. São investigados, também, os principais modelos neuro-fuzzy utilizados em sistemas de controle - Falcon e o Nefcon. Na análise do sistema NFHB- Class, é verificado o aprendizado da estrutura, o particionamento recursivo, a possibilidade de se ter um maior número de entrada - em comparação com outros sistemas neuro-fuzzy - e regras fuzzy recursivas. O sistema NFHB- Class é um modelo desenvolvido especificamente para classificação de padrões, como possui várias saídas, não é possível utilizá-lo em aplicações em controle e em previsão. Para suprir esta deficiência, é criado um novo modelo que contém uma única saída. Na terceira etapa é definido um novo modelo Neuro-Fuzzy Hierárquico BSP com conseqüentes fuzzy (NFHB-Mamdani), cuja implementação utiliza a arquitetura do NFHBClass para a fase do aprendizado, teste e validação, porém, com os conseqüentes diferentes, modificando a estratégia de definição dos conseqüentes das regras. Além de sua utilização em classificação de padrões, previsão e controle, o sistema NFHB-Mamdani é capaz de extrair conhecimento de uma base de dados em forma de regras do tipo SE ENTÃO. No estudo de casos são utilizadas duas bases de dados típicas para aplicações em classificação: Wine e o Iris. Para previsão são utilizadas séries de cargas elétricas de seis companhias brasileiras diferentes: Copel, Cemig, Light, Cerj, Eletropaulo e Furnas. Finalmente, para testar o desempenho do sistema em controle faz-se uso de uma planta de terceira ordem como processo a controlar. Os resultados obtidos para classificação, na maioria dos casos, são superiores aos melhores resultados encontrados pelos outros modelos e algoritmos aos quais foram comparados. Para previsão de cargas elétricas, os resultados obtidos estão sempre entre os melhores resultados fornecidos por outros modelos aos quais formam comparados. Quanto à aplicação em controle, o modelo NFHB-Mamdani consegue controlar, de forma satisfatória, o processo utilizado para teste.
This paper investigates the use of Binary Space Partitioning (BSP) Hierarchical Neuro-Fuzzy Systems for applications in pattern classification, forecast, control systems and obtaining of fuzzy rules. The goal is to create a BSP Hierarchical Neuro-Fuzzy Model of the Mamdani type from the BSP Hierarchical Neuro-Fuzzy Class (NFHB-Class) which is able to create its own structure automatically and obtain knowledge from a data base through fuzzy rule, interpreted linguistically, that explain the data structure. This paper is made up of four main parts: study of the main Hierarchical Systems; analysis of the BSP Hierarchical Neuro-Fuzzy Class System, definition and implementation of the NFHB-Mamdani model, and case studies. A bibliographical survey is made in the study of the main Hierarchical Systems. The main Neuro-Fuzzy Models used in control systems - Falcon and Nefcon -are also investigated. In the NFHB-Class System, the learning of the structure is verified, as well as, the recursive partitioning, the possibility of having a greater number of inputs in comparison to other Neuro-Fuzzy systems and recursive fuzzy rules. The NFHB-Class System is a model developed specifically for pattern classification, since it has various outputs, it is not possible to use it in control application and forecast. To make up for this deficiency, a new unique output model is developed. In the third part, a new BSP Hierarchical Neuro-Fuzzy model is defined with fuzzy consequents (NFHB-Mamdani), whose implementation uses the NFHB-Class architecture for the learning, test, and validation phase, yet with the different consequents, modifying the definition strategy of the consequents of the rules. Aside from its use in pattern classification, forecast, and control, the NFHB-Mamdani system is capable of obtaining knowledge from a data base in the form of rules of the type IF THEN. Two typical data base for application in classification are used in the case studies: Wine and Iris. Electric charge series of six different Brazilian companies are used for forecasting: Copel, Cemig, Light, Cerj, Eletropaulo and Furnas. Finally, to test the performance of the system in control, a third order plant is used as a process to be controlled. The obtained results for classification, in most cases, are better than the best results found by other models and algorithms to which they were compared. For forecast of electric charges, the obtained results are always among the best supplied by other models to which they were compared. Concerning its application in control, the NFHB-Mamdani model is able to control, reasonably, the process used for test.
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Li, Qie. "A Bayesian Hierarchical Model for Multiple Comparisons in Mixed Models." Bowling Green State University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1342530994.

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Busatto, Giorgio [Verfasser]. "An abstract model of hierarchical graphs and hierarchical graph transformation / von Giorgio Busatto." Oldenburg : Univ., Fachbereich Informatik, 2002. http://d-nb.info/967851955/34.

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Cora, Vlad M. "Model-based active learning in hierarchical policies." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/737.

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Hierarchical task decompositions play an essential role in the design of complex simulation and decision systems, such as the ones that arise in video games. Game designers find it very natural to adopt a divide-and-conquer philosophy of specifying hierarchical policies, where decision modules can be constructed somewhat independently. The process of choosing the parameters of these modules manually is typically lengthy and tedious. The hierarchical reinforcement learning (HRL) field has produced elegant ways of decomposing policies and value functions using semi-Markov decision processes. However, there is still a lack of demonstrations in larger nonlinear systems with discrete and continuous variables. To narrow this gap between industrial practices and academic ideas, we address the problem of designing efficient algorithms to facilitate the deployment of HRL ideas in more realistic settings. In particular, we propose Bayesian active learning methods to learn the relevant aspects of either policies or value functions by focusing on the most relevant parts of the parameter and state spaces respectively. To demonstrate the scalability of our solution, we have applied it to The Open Racing Car Simulator (TORCS), a 3D game engine that implements complex vehicle dynamics. The environment is a large topological map roughly based on downtown Vancouver, British Columbia. Higher level abstract tasks are also learned in this process using a model-based extension of the MAXQ algorithm. Our solution demonstrates how HRL can be scaled to large applications with complex, discrete and continuous non-linear dynamics.
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Kelly, Joseph. "Advances in the Normal-Normal Hierarchical Model." Thesis, Harvard University, 2014. http://dissertations.umi.com/gsas.harvard:11498.

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CONTRERAS, ROXANA JIMENEZ. "TYPE-2 HIERARCHICAL NEURO-FUZZY BSP MODEL." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2007. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=10862@1.

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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
Este trabalho tem por objetivo criar um novo sistema de inferência fuzzy intervalar do tipo 2 para tratamento de incertezas com aprendizado automático e que proporcione um intervalo de confiança para as suas saídas defuzzificadas através do cálculo dos conjuntos tipo-reduzidos correspondentes. Para viabilizar este objetivo, este novo modelo combina os paradigmas de modelagem dos sistemas de inferência fuzzy do tipo 2 e redes neurais com técnicas de particionamento recursivo BSP. Este modelo possui principalmente a capacidade de modelar e manipular a maioria dos tipos de incertezas existentes em situações reais, minimizando os efeitos destas para produzir um melhor desempenho. Além disso, tem a capacidade autônoma de criar e expandir automaticamente a sua própria estrutura, de reduzir a limitação quanto ao número de entradas e de extrair regras de conhecimento a partir de um conjunto de dados. Este novo modelo fornece um intervalo de confiança, que se constitui em uma informação importante para aplicações reais. Neste contexto, este modelo supera as limitações dos sistemas de inferência fuzzy do tipo 2 - complexidade computacional, reduzido número de entradas permissíveis e forma limitada, ou inexistente, de criarem a sua própria estrutura e regras - e dos sistemas de inferência fuzzy do tipo 1 - adaptação incompleta a incertezas e não fornecimento de um intervalo de confiança para a saída. Os sistemas de inferência fuzzy do tipo1 também apresentam limitações quanto ao reduzido número de entradas permissíveis, mas o uso de particionamentos recursivos, já explorado com excelentes resultados [SOUZ99], reduz significativamente estas limitações. O trabalho constitui-se fundamentalmente em quatro partes: um estudo sobre os diferentes sistemas de inferência fuzzy do tipo 2 existentes, análise dos sistemas neuro-fuzzy hierárquicos que usam conjuntos fuzzy do tipo 1, modelagem e implementação do novo modelo neuro-fuzzy hierárquico BSP do tipo 2 e estudo de casos. O novo modelo, denominado modelo neuro-fuzzy hierárquico BSP do tipo 2 (NFHB-T2), foi definido a partir do estudo das características desejáveis e das limitações dos sistemas de inferência fuzzy do tipo 2 e do tipo 1 e dos sistemas neuro-fuzzy hierárquicos que usam conjuntos fuzzy do tipo 1 existentes. Desta forma, o NFHB-T2 é modelado e implementado com os atributos de interpretabilidade e autonomia, a partir da concepção de sistemas de inferência fuzzy do tipo 2, de redes neurais e do particionamento recursivo BSP. O modelo desenvolvido é avaliado em diversas bases de dados benchmark e aplicações reais de previsão e aproximação de funções. São feitas comparações com outros modelos. Os resultados encontrados mostram que o modelo NFHB-T2 fornece, em previsão e aproximação de funções, resultados próximos e em vários casos superiores aos melhores resultados proporcionados pelos modelos utilizados para comparação. Em termos de tempo computacional, o seu desempenho também é muito bom. Em previsão e aproximação de funções, os intervalos de confiança obtidos para as saídas defuzzificadas mostram-se sempre coerentes e oferecem maior credibilidade na maioria dos casos quando comparados a intervalos de confiança obtidos por métodos tradicionais usando as saídas previstas pelos outros modelos e pelo próprio NFHB-T2 .
The objective of this thesis is to create a new type-2 fuzzy inference system for the treatment of uncertainties with automatic learning and that provides an interval of confidence for its defuzzified output through the calculation of corresponding type-reduced sets. In order to attain this objective, this new model combines the paradigms of the modelling of the type-2 fuzzy inference systems and neural networks with techniques of recursive BSP partitioning. This model mainly has the capacity to model and to manipulate most of the types of existing uncertainties in real situations, diminishing the effects of these to produce a better performance. In addition, it has the independent capacity to create and to expand its own structure automatically, to reduce the limitation referred to the number of inputs and to extract rules of knowledge from a data set. This new model provides a confidence interval, that constitutes an important information for real applications. In this context, this model surpasses the limitations of the type-2 fuzzy inference systems - complexity computational, small number of inputs allowed and limited form, or nonexistent, to create its own structure and rules - and of the type-1 fuzzy inference systems - incomplete adaptation to uncertainties and not to give an interval of confidence for the output. The type-1 fuzzy inference systems also present limitations with regard to the small number of inputs allowed, but the use of recursive partitioning, already explored with excellent results [SOUZ99], reduce significantly these limitations. This work constitutes fundamentally of four parts: a study on the different existing type-2 fuzzy inference systems, analysis of the hierarchical neuro- fuzzy systems that use type-1 fuzzy sets, modelling and implementation of the new type-2 hierarchical neuro-fuzzy BSP model and study of cases. The new model, denominated type-2 hierarchical neuro-fuzzy BSP model (T2-HNFB) was defined from the study of the desirable characteristics and the limitations of the type-2 and type-1 fuzzy inference systems and the existing hierarchical neuro-fuzzy systems that use type- 1 fuzzy sets. Of this form, the T2-HNFB model is modelling and implemented with the attributes of interpretability and autonomy, from the conception of type-2 fuzzy inference systems, neural networks and recursive BSP partitioning. The developed model is evaluated in different benchmark databases and real applications of forecast and approximation of functions. Comparisons with other models are done. The results obtained show that T2-HNFB model provides, in forecast and approximation of functions, next results and in several cases superior to the best results provided by the models used for comparison. In terms of computational time, its performance also is very good. In forecast and approximation of functions, the intervals of confidence obtained for the defuzzified outputs are always coherent and offer greater credibility in most of cases when compared with intervals of confidence obtained through traditional methods using the forecast outputs by the other models and the own T2-HNFB model.
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Books on the topic "The hierarchical model"

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Chan, Hing Kai, and Xiaojun Wang. Fuzzy Hierarchical Model for Risk Assessment. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5043-5.

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Chen, Y. F. Translation of a hierarchical model to VHDL. Manchester: UMIST, 1996.

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Mark, Wilson. Measuring stages of growth: A psychological model of hierarchical development. Hawthorn, Vic., Australia: Australian Council for Educational Research, 1985.

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Glen, J. J. A model for promotion rate control in hierarchical manpower systems. Edinburgh: University of Edinburgh, Management School, 1994.

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Chan, Hing Kai. Fuzzy Hierarchical Model for Risk Assessment: Principles, Concepts, and Practical Applications. London: Springer London, 2013.

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Zawis, John A. Accessing hierarchical databases via SQL transactions in a multi-model database system. Monterey, Calif: Naval Postgraduate School, 1987.

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Maos, J. The hierarchical organization of rural service centres: An operational model for regional development planning. Rehovot, Israel: Settlement Study Centre, 1987.

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Osano, Hiroshi. The welfare analysis of the social security system in a hierarchical firm model with bargaining. Hikone, Japan: Faculty of Economics, Shiga University, 1985.

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Gupta, Amit. Effect of service climate on service quality: Test of a model using hierarchical linear modeling. Bangalore: Indian Institute of Management, 2002.

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Burton, Richard M., and Børge Obel, eds. Design Models for Hierarchical Organizations. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-2285-0.

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Book chapters on the topic "The hierarchical model"

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Yao, Yuan, Xing Su, and Hanghang Tong. "Hierarchical Model." In Mobile Data Mining, 25–30. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02101-6_4.

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Bressoud, Thomas, and David White. "Hierarchical Model: Constraints." In Introduction to Data Systems, 547–79. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-54371-6_17.

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Weik, Martin H. "hierarchical data model." In Computer Science and Communications Dictionary, 723. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_8346.

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Bauerschmidt, Roland, David C. Brydges, and Gordon Slade. "The Hierarchical Model." In Introduction to a Renormalisation Group Method, 53–65. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9593-3_4.

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Kritchevski, Evgenij. "Hierarchical Anderson model." In Probability and Mathematical Physics, 309–22. Providence, Rhode Island: American Mathematical Society, 2007. http://dx.doi.org/10.1090/crmp/042/17.

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Hainaut, Jean-Luc. "Hierarchical Data Model." In Encyclopedia of Database Systems, 1689–95. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_189.

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Hainaut, Jean-Luc. "Hierarchical Data Model." In Encyclopedia of Database Systems, 1294–300. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_189.

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Hainaut, Jean-Luc. "Hierarchical Data Model." In Encyclopedia of Database Systems, 1–7. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4899-7993-3_189-2.

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Cerny, E., B. Berkane, P. Girodias, and K. Khordoc. "HAAD VHDL Model." In Hierarchical Annotated Action Diagrams, 49–68. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5615-2_4.

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Barros, Tomás, Ludovic Henrio, and Eric Madelaine. "Behavioural Models for Hierarchical Components." In Model Checking Software, 154–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11537328_14.

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Conference papers on the topic "The hierarchical model"

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Diskin, Zinovy, Tom Maibaum, Alan Wassyng, Stephen Wynn-Williams, and Mark Lawford. "Assurance via model transformations and their hierarchical refinement." In MODELS '18: ACM/IEEE 21th International Conference on Model Driven Engineering Languages and Systems. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3239372.3239413.

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Namora, F., S. Nurrohmah, and I. Fithriani. "Hierarchical credibility model." In PROCEEDINGS OF THE 6TH INTERNATIONAL SYMPOSIUM ON CURRENT PROGRESS IN MATHEMATICS AND SCIENCES 2020 (ISCPMS 2020). AIP Publishing, 2021. http://dx.doi.org/10.1063/5.0059047.

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Adesina, Opeyemi, Timothy C. Lethbridge, and Stephane Some. "Optimizing Hierarchical, Concurrent State Machines in Umple for Model Checking." In 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C). IEEE, 2019. http://dx.doi.org/10.1109/models-c.2019.00082.

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Scattolini, Riccardo, and Patrizio Colaneri. "Hierarchical model predictive control." In 2007 46th IEEE Conference on Decision and Control. IEEE, 2007. http://dx.doi.org/10.1109/cdc.2007.4434079.

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Garcia, Vincent, Frank Nielsen, and Richard Nock. "Hierarchical Gaussian mixture model." In 2010 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2010. http://dx.doi.org/10.1109/icassp.2010.5495750.

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Chou, W. K., and D. Y. Y. Yun. "Hierarchical neural model: L3." In 1991 IEEE International Joint Conference on Neural Networks. IEEE, 1991. http://dx.doi.org/10.1109/ijcnn.1991.170733.

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Nezhad Karim Nobakht, B., and M. Christie. "Model Prediction under Uncertainty Using Hierarchical Models." In 79th EAGE Conference and Exhibition 2017. Netherlands: EAGE Publications BV, 2017. http://dx.doi.org/10.3997/2214-4609.201701024.

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Lazarides, George. "Degenerate or hierarchical neutrinos in supersymmetric inflation." In European Network on Physics beyond the Standard Model. Trieste, Italy: Sissa Medialab, 1999. http://dx.doi.org/10.22323/1.002.0008.

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Zhang, Ganglin, Guangcan Liu, Weibing Chen, and Cheng Yang. "Optimal Power Consumption Analysis of Two-level Hierarchical Model and Non-hierarchical Model." In 2nd International Symposium on Computer, Communication, Control and Automation. Paris, France: Atlantis Press, 2013. http://dx.doi.org/10.2991/isccca.2013.125.

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Chen, Xiao-hua, and Chun-zhi Li. "Face representation using hierarchical model." In 2011 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC). IEEE, 2011. http://dx.doi.org/10.1109/icspcc.2011.6061708.

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Reports on the topic "The hierarchical model"

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Jewell, William S. A Heteroscedastic Hierarchical Model. Fort Belvoir, VA: Defense Technical Information Center, April 1987. http://dx.doi.org/10.21236/ada184256.

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Raychev, Nikolay. Hybrid system with fuzzy hierarchical evaluation model. Web of Open Science, June 2020. http://dx.doi.org/10.37686/nal.v1i1.40.

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Liu, Mingyan, and John S. Baras. A Hierarchical Loss Network Model for Performance Evaluation. Fort Belvoir, VA: Defense Technical Information Center, January 2000. http://dx.doi.org/10.21236/ada441030.

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Sigeti, David Edward, and Scott Alan Vander Wiel. Doubly-Hierarchical One-Way Random Effects Model: Multivariate Data. Office of Scientific and Technical Information (OSTI), October 2016. http://dx.doi.org/10.2172/1329823.

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Abbas, Mustafa. Consistency Analysis for Judgment Quantification in Hierarchical Decision Model. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.2695.

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Berliner, L. M., Radu Herbei, Ralph F. Milliff, and Christopher K. Wikle. Bayesian Hierarchical Model Characterization of Model Error in Ocean Data Assimilation and Forecasts. Fort Belvoir, VA: Defense Technical Information Center, September 2010. http://dx.doi.org/10.21236/ada542570.

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Wikle, Christopher K., Ralph F. Milliff, L. M. Berliner, and Radu Herbei. Bayesian Hierarchical Model Characterization of Model Error in Ocean Data Assimilation and Forecasts. Fort Belvoir, VA: Defense Technical Information Center, September 2010. http://dx.doi.org/10.21236/ada542614.

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Milliff, Ralph F., Christopher K. Wikle, L. M. Berliner, and Radu Herbei. Bayesian Hierarchical Model Characterization of Model Error in Ocean Data Assimilation and Forecasts. Fort Belvoir, VA: Defense Technical Information Center, July 2012. http://dx.doi.org/10.21236/ada564536.

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Milliff, Ralph F., Christopher K. Wikle, L. M. Berliner, and Radu Herbei. Bayesian Hierarchical Model Characterization of Model Error in Ocean Data Assimilation and Forecasts. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada568491.

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Wikle, Christopher K., Ralph F. Milliff, L. M. Berliner, and Radu Herbei. Bayesian Hierarchical Model Characterization of Model Error in Ocean Data Assimilation and Forecasts. Fort Belvoir, VA: Defense Technical Information Center, September 2013. http://dx.doi.org/10.21236/ada601463.

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