The dynamic graphical simulator which realizes the dynamic procedure simulation of artificial neural network is driven by and belongs to this environment.
动态图形模拟器是从属于集成环境，并受之驱动的专用模拟器，它实现了神经网络动态变化过程的模拟。
Simulation results show that the fuzzy control and neural network can take advantage of each other to possess a good performance in the uncertain nonlinear system.
仿真结果表明，该方案可以实现模糊控制和神经网络的优势互补，对不确定非线性系统具有很好的控制效果。
Based on the theory of statistics, this dissertation investigates neural network classifiers realized with software simulation in the computer.
本文以统计理论为基础，主要讨论在计算机上用软件模拟实现的神经网络分类器。
This paper proposes a new type position controller with neural network structure, gives it 's learning rule, and do the simulation experiment on position servo system of NC machine.
本文提出了一种新型的神经网络结构的位置控制器，给出了该控制器的学习算法，并进行了数控机床位置伺服系统的仿真实验。
It mainly introduces new methods of diagnosing simulation circuit trouble and testing principles, explains the scheme of choosing neural network structure parameter especially.
主要介绍了模拟电路故障诊断的新方法及测试原理，重点讲解了神经网络结构中参数选择方案。
The results of simulation show that the existing control problem of the plant can be successfully solved by neural network, adaptive inverse control and the result is good.
仿真结果表明，神经网络、自适应逆控制方法可以成功地解决装置中现有的控制问题，并取得良好的效果。
Correctness and validity of the neural network object forecast algorithm and immune algorithm with evaluated antigen are tested by the simulation of varies examples.
通过对不同情况算例的仿真，验证了神经网络目标预测算法和基于抗原进化免疫算法的正确性和有效性。
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.
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。
The result of simulation shows that this neural network fuzzy controller features self-learning and self-adaptive capabilities, and the purpose of on-line control is accomplished.
仿真结果表明，所设计的神经网络模糊控制器具有自学习、自适应等优点，达到了在线控制的目的。
The combined neural network may overcome the limitation of the single neural network in simulation and identification performances, and has the advantage of implementing more comprehensive function.
组合神经网络可以克服单个神经网络功能的单一局限性，实现更加全面综合的仿真识别功能。
The training model of test simulation for car of inverted pendulum based on BP algorithm of artificial neural networks (ANN) is a BP network that has 4-input and 3-layer structure.
基于人工神经网络BP算法的倒立摆小车实验仿真训练模型，其倒立摆BP网络为4输入3层结构。
This paper discusses about an algorithm of calculating the pulse switch Angle by using of forward network BP algorithm based on the Neural network, and makes the MATLAB simulation.
论述了用神经网络中的前向网络BP算法来计算脉冲开关角的一种计算方法，并进行了MATLAB仿真。
Simulation results show that extensive mapping ability of neural network and rapid global convergence of ant system can be obtained by combining ant system and neural network.
仿真实验表明：用蚁群算法训练神经网络，可兼有神经网络广泛映射能力和蚁群算法快速全局收敛的性能。
The neural network algorithm is formulated and the controller is designed. Simulation research is carried out by taking full advantage of a computer.
阐述了神经网络学习算法，设计了高速公路可变速度标志神经网络控制器，并对控制器进行了仿真研究。
Generally, this thesis introduces a bridge health detect method based on neural network and applies it to the simulation detection of Ma Sangxi bridge successfully.
概括而言，本文研究和实现了一种基于神经网络的桥梁健康检测方法，并成功运用于马桑溪大桥的模拟检测中。
The paper comprehensively USES the superiority of fuzzy pattern recognition over rejecting noise information and BP neural network on simulation to design a new algorithm named fuzzy neural network.
文章在综合利用模糊模式识别剔除噪音信息和BP神经网络拟合优势的基础上，设计了模糊神经网络新算法。
Neural network (NN) is product of simulation of how-to-manage-information in human brain.
神经网络是对人脑的信息处理方式进行模拟的产物。
Based on this system, a dynamic neural network is used to track the change of quality characteristics during manufacturing process. Numerical simulation and practical experiment show good results.
根据提出的系统模型利用动态神经网络对加工过程质量特征参数的变化进行了跟踪实验，效果良好。
The inversion method combining the genetic neural network and the discrete element simulation of triaxial tests is proposed for determining the contact model parameters of the conditioned soil.
将神经网络、遗传算法、三轴实验和离散元数值模拟相结合，用于改性砂土等效离散元接触模型参数反演。
The simulation results show that the training time of Branched Feedforward Neural Network is obviously reduced and the classifying effect is much better as compared with general BP Network.
仿真结果表明，与一般BP网络相比较，分支前馈神经网络显著地减少了训练时间，且分类效果更好。
The mathematical models are amended by BP neural network algorithm, and method of finite element numerical simulation is used to compensate the removal data and extend the applied range.
应用BP -神经网络法对数学模型进行修正，建立有限元数值模拟方法补充剔除数据并对数学模型适用范围进行扩展。
The application of RBF neural network in hardy nonlinear system and the result of simulation is introduced. An example of pH control in hydroxylamine reactor is described.
以某厂羟胺反应器的氢离子浓度控制为例，介绍了径向基函数（RBF）神经网络在强非线性对象预测控制中的应用及仿真研究结果。
Finally, an adaptive noise cancelling system is designed as an example and the computer simulation results show the superior performance of the adaptive neural network filters.
最后以自适应噪声对消系统为例，进行了计算机仿真，结果显示了这种滤波器的良好性能。
Neural network method of feature input is employed, and simulation experimental results indicate that it is effective.
文中所采用的特征输入神经网络方法，通过模拟实验，取得了良好的效果。
Simulation results show that this fuzzy-neural network estimator can precisely measure the value of resistance and improve the low-speed performances of DTC system efficiently.
仿真结果表明了模糊神经网络观测器可实现对定子电阻的精确检测，从而提高直接转矩控制系统的低速性能。
Principles of fuzzy neural network and FNN method are adopted for the numerical simulation of network modeling and forecasting of beams with finite deformation of two different materials.
本文运用模糊神经网络原理，采用学习结合型FNN方法，针对两种不同材料梁的大变形进行了网络建模和预测的数值仿真。
Simulation results show that the design of the fuzzy neural network controller can reduce the average delay of vehicles effectively, and meet the demand for real-time control.
仿真结果表明，本文设计的模糊神经网络控制器能够有效降低车辆平均延误，满足实时控制的要求。
The simulation results are presented to demonstrate that the model of an unknown nonlinear dynamical system is built with the multilayered feedforward neural network model.
仿真实例进一步表明，采用神经网络建立未知非线性动态系统的在线模型具有可行性。
The simulation results are presented to demonstrate that the model of an unknown nonlinear dynamical system is built with the multilayered feedforward neural network model.
仿真实例进一步表明，采用神经网络建立未知非线性动态系统的在线模型具有可行性。
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