神经

Two-phase-learning is adopted. The strongpoint is that intuitionistic knowledge can be used to control the segmentation by using input-weight and distant-weight. Examples prove that segmentation using SOFM network is right and efficient. Surface fitting is an absolutely necessarily process in REGM. Surface fitting includes algebraic and parametric surface fitting. Algebraic surface fitting has not extra requirement for data distribute format and boundary shape.

第三种方法把人工智能用于反向工程的数据分区，因为数据分区的实质是相似特征的聚类，而神经网络可以实现自组织特征映射，在本论文中第一次把自组织特征映射神经网络用于多视图点云的数据分区，通过神经网络对数据点的坐标、法向量等几何特征进行聚类，并且运用了快速稳定的训练方法——两阶段训练算法，最重要的是，通过距离权值和输入权值可以把用户观察所得的直观知识加入聚类过程。

By making up of time-frequency local property and multi-scale analytical capability of wavelet transformation and self-learning and prediction function of artificial neural network,chapter three develops a prediction model loose combining wavelet analysis and Radial Basis Functionsneural network,abbreviated as Wavelet-RBFNN.

第三章利用小波变换的时频局域化特性和多尺度分析能力及神经网络的自学习、预测功能，通过小波分析与神经网络的松散型结合方式，建立小波分析和径向基函数神经网络(Wavelet-RBFNN)预测模型。

The theory and method of neural network,and the feasibility and superiority of building the coal bed geological model are also discussed by considering the geological variable characteristics,such as space territory,continuity,anisotropism and great non-liner relation.

摘 要：分析了建立矿床地质模型传统的估值方法的不足，针对地质变量之间具有空间局域性、连续性和各向异性，以及高度非线性关系的特征，阐述了应用人工神经网络原理和方法建立矿床地质模型的可能性和优越性，建立了地质变量参数估值计算的神经网络模型的基本结构，描述了地质变量与其影响因素之间的非线性映射关系，从而提出了基于人工神经网络原理的地质模型估值方法。

The effects, of protection and treatment with, combined Ch, NaF and a trophie were significantly better than that with atropine alone. In isolated phrenic nerve-diaphragm preparation of rat, isolated jejuneal preparation of, guinea pig, and the metabolic experiment of rat liver, the results of the, assay on the rat blood ChE activity indicated that antidotal effect of NaF may be due to the antidese...

大鼠离体膈神经膈肌、豚鼠离体空肠和大鼠肝脏代谢实验及对大鼠全血胆碱酯酶活力测定的结果表明，NaF的抗毒作用可能是由于其对神经肌肉接头和/或神经节中的N受体的抗脱敏作用；而Ch则可能通过其对ChE的保护和/或加速被抑制ChE的复能，阻碍Ach到达M受体部位而发挥作用。

The paper puts forward stock prediction pattern based on BP Neural Network, depending on Neural Networks approachability to non-linear function. Compared with the prediction method of Time Order, one of the traditional prediction methods, BP algorithm is verified to be feasible and effective on stock prediction.

本文借助神经网络对非线性函数的逼近能力，提出基于BP神经网络的股票价格预测模型，通过对比传统的时间序列预测方法，证实BP神经网络算法用于股票预测的可行性及有效性。

The feasibility of neural network application to asphalt concrete pavement performance forecast is set forth. Three main environmental factors-traffic, rainfall, temperature, which can lead to the direct change of pavement performance are used as the input layer of neural network. The performance indexes of asphaltum concrete pavement, i.e. dilapidation rate, smoothness, deflection, friction coefficient are used as output layer. The relation between environment factor and pavement performance is acquired by introducing the module of BP Neural Network in MATLAB.

阐述了应用BP神经网络进行沥青混凝土路面使用性能预测的可行性，以环境因素，即交通量、降雨量、温度等3个直接使路面性能发生变化的主要因素作为BP神经网络的输入层，以沥青混凝土路面的破损率、平整度、弯沉值、摩擦系数等4项性能指标作为输出层，采用MATLAB语言中的BP神经网络模块进行自学习，得出环境因素与路面使用性能之间的关系。

Three main environmental factors——traffic,rainfall,temperature,which can lead to the direct change of pavement performance are used as the input layer of neural network.The performance indexes of asphaltum concrete pavement,i.e.dilapidation rate,smoothness,deflection,friction coefficient are used as output layer.The relation between environment factor and pavement performance is acquired by introducing the module of BP Neural Network in MATLAB.

阐述了应用BP神经网络进行沥青混凝土路面使用性能预测的可行性，以环境因素，即交通量、降雨量、温度等3个直接使路面性能发生变化的主要因素作为BP神经网络的输入层，以沥青混凝土路面的破损率、平整度、弯沉值、摩擦系数等4项性能指标作为输出层，采用MATLAB语言中的BP神经网络模块进行自学习，得出环境因素与路面使用性能之间的关系。

But, autogenous nerve grafting can cause new damage, and its source was limited.

但是，移植自体神经会造成新的非功能或重要功能部位的神经损害，可提供移植的神经来源有限。

We firstly summarize the NN system modeling methods and the popular optimization algorithms, and point out the deficiencies, the limitation of noise cancellation modeling and the need of human intelligence participation, in our former optimization work after analysis. An abstract problem is extracted. It is to find out control variant that makes system output optimized using the data comprising a sample set with N x samples and their corresponding y, and it does not explore given system and achieves full automatic computation procedure. Next we propose a multi-variant controlled system optimization method utilizing CASSANDRA-I small type neural computer and program its application software. Moreover systematic experiments are made to demonstrate the method's validity, ability of noisy samples disposal and practical application.

我们总结了神经网络系统建模方法以及得到广泛采用的优化算法，并分析指出我们前阶段的优化工作中存在的去噪声建模有局限性和计算过程需人干预的不足，在此基础上把问题抽象为：已从系统获得含N个控制量样本的样本集S以及每个样本对应的系统输出，如何依据这些数据求出一个使系统输出y得到优化的控制量x，而不研究具体系统，实现不需要人智能参与自动完成的计算过程；提出应用人工神经网络硬件系统——CASSANDRA-I小型神经计算机的多变量控制系统优化方法，设计完成了其应用软件；并进行系统的实验工作，验证算法的有效性以及此方法处理带噪声样本和实际应用能力。

People have proposed many different control method to realize the chaos synchronization. This article has conducted the thorough research to the chaotic neural network, introduced the chaos neural network synchronization method. Based on the linear matrix inequality technology and Lyapunov stability theory, we have Studied the synchronized controller design method of the global synchronization, the non-linear global synchronization and the global exponential synchronization. Here we have proposed one kind of synchronization algorithm, through which might obtain the gain matrix quickly and automatize.

本文对混沌神经网络进行了深入的研究，介绍了混沌神经网络同步方法的发展及研究现状，基于线性矩阵不等式技术和李雅普诺夫稳定性理论，分别研究了全局同步、非线性全局同步和全局指数同步的同步控制器实现方法，提出了一种实现混沌神经网络同步的算法，通过该算法可以快速得到增益矩阵的形式，实现混沌系统的同步控制，数值仿真验证了算法的有效性。

The split between the two groups can hardly be papered over.

这两个团体间的分歧难以掩饰。

This approach not only encourages a greater number of responses, but minimizes the likelihood of stale groupthink.

这种做法不仅鼓励了更多的反应，而且减少跟风的可能性。