Neural networks are applied to the system identification problems using adaptive algorithms for either parameter or functional estimation of dynamic systems. In this paper the neural networks' sensitivity to input values and connections' weights, is studied. The Reduction-Sigmoid-Amplification (RSA) neurons are introduced and four different models of neural network architecture are proposed and...

a new dynamic friction model for modeling of elliptical vibration assisted turning (evat) was developed in this research. the periodic change of the friction force direction is known to be one of the most important causes of this phenomenon. in modeling of machining processes (including the evat process), static coulomb friction model was employed by most of the researchers. because of the peri...

This paper presents the Dynamic Relational Topic Model, a new dynamic topic model that incorporates both document text and relationships for discovering the underlying topics in document collections and their evolution over time. We derive an approximate variational inference algorithm for our model and demonstrate its effectiveness over previous approaches by analyzing papers in Computer Scien...

Most multipopulation epidemic models are of the contact distribution type, in which the locations of successive contacts are chosen independently from appropriate contact distributions. This paper is concerned with an alternative class of models, termed dynamic population epidemic models, in which infectives move among the populations and can infect only within their current population. Both th...

The evolution of an elastic-plastic material is modeled as an initial boundary value problem consisting of the dynamic momentum equation coupled with a constitutive law for which the hysteretic dependence between stress and strain is described by a system of variational inequalities. This system is posed as an evolution equation in Hilbert space for which is proved the existence and uniqueness ...

Many dynamic systems involve a number of entities that are largely independent of each other but interact with each other via a subset of state variables. We present global/local dynamic models (GLDMs) to capture these kinds of systems. In a GLDM, the state of an entity is decomposed into a globally influenced state that depends on other entities, and a locally influenced state that depends onl...

ISSN: 1070-5511 (Print) 1532-8007 (Online) Journal homepage: http://www.tandfonline.com/loi/hsem20 Dynamic Structural Equation Models Tihomir Asparouhov, Ellen L. Hamaker & Bengt Muthén To cite this article: Tihomir Asparouhov, Ellen L. Hamaker & Bengt Muthén (2017): Dynamic Structural Equation Models, Structural Equation Modeling: A Multidisciplinary Journal To link to this article: https://do...

K e y w o r d s: Family of Sign RCA Models, Value at Risk, backtesting, loss function.

We develop a dynamic Bayesian beta model for modeling and forecasting single time series of proportions. This work is related to the class of the so called dynamic generalized linear models (DGLM). We use non-conjugate priors and some forms of approximate Bayesian analysis, including Linear Bayesian estimation. Some applications to both real and simulated data are provided.

We analyze random networks that change over time. First we analyze a dynamic Erdős-Renyi model, whose edges change over time. We describe its stationary distribution, its convergence thereto, and the SI contact process on the network, which has relevance for connectivity and the spread of infections. Second, we analyze the effect of node turnover, when nodes enter and leave the network, which h...