Two kinds of parameter learning algorithms are proposed. One is the real value genetic algorithm ( RVGA); the other is the stochastic learning automaton. 针对模糊神经网络参数学习中容易陷入局部极小以及算法结构复杂等问题,提出了两种网络参数学习方法:实值遗传算法和随机学习自动机算法。
By introducing a break noise, create and disappear probability, we obtain a wholly stochastic cellular automaton traffic model. 在一维局部作用元胞自动机交通流模型中引入刹车噪声与产生、消失概率,得到一完全随机的元胞自动机交通流模型。
The processing of model the software architecture is the core, which involved a number of algorithms, the relationship between Markov chain and the stochastic finite automaton, deducing the functional relationship between metric entropy and the traditional software reliability model. 文章的重点在于软件结构模型的建模流程,其中涉及了多个算法及Markov链与概率有限自动机关系的阐述,度量熵与传统可靠性模型间关系的推导。