在一阶马尔可夫假设下，利用多层前向神经网络进行迭代逼近求解。
The solution was put forward by the iterative approach through multiple-forward network under Markov hypothesis.
按照可列一阶马尔可夫链方式，建立了一个具体的二阶数字锁相环的模型，并对它进行了分析。
A specific second-order digital phase-locked loop is modeled after a first-order Markov chain with alternatives, aud analyzed.
基于贝叶斯重建，本研究提出了一种应用于贝叶斯重建中新的综合了二次一阶先验和二次二阶先验的马尔可夫随机场混合多阶先验。
This article proposed a new type of MRF (Markov random fields) hybrid multi-order prior for Bayesian reconstruction, which combines quadratic smoothness priors of different orders.
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