RESUMO
A discrete-time Markov chain model, a continuous-time Markov chain model, and a stochastic differential equation model are compared for a population experiencing demographic and environmental variability. It is assumed that the environment produces random changes in the per capita birth and death rates, which are independent from the inherent random (demographic) variations in the number of births and deaths for any time interval. An existence and uniqueness result is proved for the stochastic differential equation system. Similarities between the models are demonstrated analytically and computational results are provided to show that estimated persistence times for the three stochastic models are generally in good agreement when the models satisfy certain consistency conditions.
Assuntos
Dinâmica Populacional , Processos Estocásticos , Animais , Meio Ambiente , Humanos , Cadeias de Markov , Matemática , Modelos Estatísticos , Crescimento DemográficoRESUMO
This paper presents a two-step strategy to provide a quality-predictable image reconstruction. A Pre-computed Back Projection based Penalized-Likelihood (PPL) method is proposed in the strategy to generate consistent image quality. To solve PPL efficiently, relaxed Ordered Subsets (OS) is applied. A training sets based evaluation is performed to quantify the effect of the undetermined parameters in OS, which lets the results as consistent as possible with the theoretical one.
Assuntos
Algoritmos , Intensificação de Imagem Radiográfica/métodos , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Tomografia por Raios X/métodos , Interpretação Estatística de Dados , Funções Verossimilhança , Imagens de Fantasmas , Reprodutibilidade dos Testes , Sensibilidade e Especificidade , Tomografia por Raios X/instrumentaçãoRESUMO
This paper presents a theoretical design of how a minimax equilibrium of differential game is achieved in stochastic cellular neural networks. In order to realize the equilibrium, two opposing players are selected for the model of stochastic cellular neural networks. One is the vector of external inputs and the other is the vector of internal noises. The design procedure follows the nonlinear H infinity optimal control methodology to accomplish the best rational stabilization in probability for stochastic cellular neural networks, and to attenuate noises to a predefined level with stability margins. Three numerical examples are given to demonstrate the effectiveness of the proposed approach.