泛函网络是最近提出的一种对神经网络的有效推广。
Functional network is a recently introduced extension of neural networks.
泛函网络是类似于人工神经网络的新型网络模型，是泛函方程的网络表达形式。
Functional network is new network model. It is similar to artificial neural network and is network expression of functional equation.
提出了一种多项式泛函网络运算新模型，来求解任意数域或环上多项式运算问题。
The new computing model of polynomial functional network is firstly proposed and the solving polynomials computing problem in arbitrary coefficients fields or ring is discussed.
因此用进化策略、差分演化算法、泛函网络来研究数值计算，有较高的理论价值和实际意义。
So, researching numerical computing by Evolution Strategy, Differential Evolution Algorithm and Functional Networks have higher theory value and practical significance.
通过对泛函网络的分析，提出了一种序列泛函网络模型及学习算法，而网络的泛函参数利用梯度下降法来进行学习。
In this paper, by analyzing the functional network, a new model and learning algorithm of the serial functional networks is proposed.
针对信息科学和控制理论中经常涉及的一类泛函极值问题，提出基于连续回归神经网络的求解方法。
In this paper, the continuous time recurrent neural network is proposed to solve the functional minimization problem, which is often involved in estimation and control.
利用适当的李亚普若夫泛函，研究了时滞分流抑制型细胞神经网络的周期解的指数稳定性。
By means of suitable Lyapunov functionals, the exponential stability of periodic solutions for shunting inhibitory cellular neural networks(SICNNs)with delays and variable coefficients is studied.
其次，采用人工神经网络方法（ANN）和扩大训练基组构建新B3LYP泛函，并将新泛函用于分子能量的计算。
Secondly, new B3LYP functional is constructed combined artificial neural network (ANN) with enlarged training set and the new B3LYP functional is adopted to calculate the molecular energies.
控制律由最优广义预测控制(OGPC)算法和泛函连接网络(FLN)直接自适应律组成。
The control law consists of the optimal generalized predictive controller (OGPC) and the functional link network (FLN) direct adaptive law.
控制律由最优广义预测控制(OGPC)算法和泛函连接网络(FLN)直接自适应律组成。
The control law consists of the optimal generalized predictive controller (OGPC) and the functional link network (FLN) direct adaptive law.
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