A model following control system (MFCS) can output general signals following the desired ones. In this paper, a method of nonlinear MFCS will be extended to be a nonlinear descriptor system in discrete time. The nonlinear system studied in this paper has the property of norm constraint $\left|\right|f\left(v\left(k\right)\right)\left|\right|\le \alpha +{\beta \left|\right|v\left(k\right)\left|\right|}^{\gamma }$, where $\alpha \ge 0$, $\beta \ge 0$ , $0\le \gamma <1$. In this case, a new criterion is proposed to ensure the internal states be stable.

Given a fixed dependency graph $G$ that describes a Bayesian network of binary variables $X_1,\cdots ,X_n$, our main result is a tight bound on the mutual information $I_c(Y_1,\cdots ,Y_k)={\sum }_{j=1}^{k}H\left(Y_j\right)/c-H(Y_1,\cdots ,Y_k)$ of an observed subset $Y_1,\cdots ,Y_k$ of the variables $X_1,\cdots ,X_n$. Our bound depends on certain quantities that can be computed from the connective structure of the nodes in $G$. Thus it allows to discriminate between different dependency graphs for a probability distribution, as we show from numerical experiments.

El problema de hacer inferencias sobre el cociente de las medias de dos poblaciones normales, conocido como problema de Fieller-Creasy, es de interés particular en las ciencias experimentales que continuamente necesitan hacer comparaciones relativas de diferentes métodos. Desde un punto de vista bayesiano, el problema se reduce a calcular la distribución final de dicho cociente. En este trabajo se determina la distribución final de referencia, esto es, utilizando tan sólo la información proporcionada...