人工神经网络BP算法可有效解决非线性数学建模问题，并具有较强的并行处理能力。
The ANN BP arithmetic can solve the problem of nonlinear mathematics effectively, and possess higher collateral processing ability.
本文从机理建模入手，应用流体力学理论对双容水箱液位系统进行力学分析，建立非线性数学模型。
By using hydrodynamics theory and method, we can analyze the mechanics principle of two-tank system and establish the system mathematics model.
针对高速公路可变速度控制是一个非线性时变系统，难于用数学模型准确建模这一特点，提出了神经网络控制方法。
The variable speed control for freeway traffic is a nonlinear and time variable system, it is difficult to model with a mathematical model. A neural network control method is put forward.
本文改变传统的数学建模方法，运用神经网络方法，建立一个粘土的非线性本构关系模型。
Instead of traditional methods, a neural network method for building the nonlinear constitutive law model of clay is proposed in this paper.
应用流体力学理论对双容水箱液位系统进行力学分析，采用机理建模方法，建立非线性数学模型。
By using hydrodynamics theory, the mechanics principle of two-tank system was analyzed and the system nonlinear mathematics model was established.
由于溶解氧过程模型中的呼吸率不易直接得到，将神经网络建模技术与溶解氧过程数学模型相结合得到溶解氧的混合非线性过程模型。
Since the respiration rate is difficult to get, neural network modeling technique and DO process mathematic model are combined to obtain the hybrid model of DO.
文章针对水电仿真系统中水轮发电机机组的非线性动态数学模型建模复杂问题，提出了一种基于信息融合思想的神经网络模型。
Aiming at problematic complexity of the nonlinear dynamic mathematical modeling of generator in the hydro-electric simulation system, a neural network based on information fusion is brought forward.
针对高速公路限速控制是一个非线性时变系统、难以用数学模型准确建模这一特点，提出了R BF神经网络控制方法。
The control for speed limitation on freeway is a nonlinear and time variable system, it is difficult to model with a mathematical model. A control method based on RBF Neural Network is put forward.
高速公路限速控制是一个非线性时变系统，难于用数学模型准确建模，提出一种模糊神经网络实现限速控制。
The control for speed limit on expressway is a nonlinear and time variable system, it is difficult to simulate with a mathematical model. A neuro-fuzzy network is proposed to solve the problem.
高速公路限速控制是一个非线性时变系统，难于用数学模型准确建模，提出一种模糊神经网络实现限速控制。
The control for speed limit on expressway is a nonlinear and time variable system, it is difficult to simulate with a mathematical model. A neuro-fuzzy network is proposed to solve the problem.
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