However, due to the difficulty of handling the directions of complexity flows in optimization, a transformed network flow model is formulated and solved based on dynamic programming.
Logistic regression, support vector machine (SVM), random and uncertain analysis model, wavelet transform, and neural network model were used to analyze the BLD.
and set operations (e.g. Union, Intersection, etc).. To map gene regulatory networks on BDDs, the first step is to transform networks into Boolean functions which represent the dynamics of a model.
In the model, the aircraft taxiing schedule problem was transformed to multi-commodity network model, and the Genetic-annealing algorithm was designed to solve the problem.
The closed-loop neural control system is transformed into a novel neural network model both with uncertainties and with time delays termed standard neural network model (SNNM).
Husain and Rao in 2014 applied a Huang Hilbert transform and an artificial neural network model on sets A and E (the same datasets used in this paper).
The results show that the proposed method outperforms over the other two existing methods: a Huang Hilbert transform and an artificial neural network model by Husain and Rao [22] and a ST and LS_SVM methods by Siuly et al. [21].
The conceptual representation of a temporal causal network model can be transformed in a systematic or even automated manner into the following numerical representation of the model [30, 31]; here the variable t indicates a time point; it varies over the real numbers.
A complex causal network model of biological entities can be transformed into a single HYP by collecting the individual HYPs representing entities in the model and regrouping the connections of all the downstream genes to a single upstream process representing the whole complex causal network model; this in essence is a flattening of the underlying graph structure.
The IKK/NF-κB signaling HYP was generated by first constructing a causal network model of IKK/NF-κB signaling, and then transforming it into a single HYP by the aggregation procedure illustrated in Figure 1b (see Methods).
