This paper establishes dynamic model of the longitudinal vibration of the overall belt conveyor system with finite elemental method and simulation module of driving system in neural networks 摘要通过有限单元法建立了整个输送机系统纵向振动的动力学模型，用仿真软件的神经网络工具箱编程建立了驱动系统仿真模块。
The modeling instrument is the nn toolbox in the matlab5 . 3 and the modeling methods are bp and rbf . we compare simply these two methods based on cement kiln . the math model has some applied value 选定的建模工具为matlab5 . 3下的神经网络工具箱，神经网络方法为bp和rbf两种，并基于水泥窑系统对两种方法进行了比较。
Secondly , based on the neural network toolbox , a convenient realization on matlab is discussed for bp neural network , and the programming methods are presented about how to create a network , train a network and simulate a network 然后在matlab神经网络工具箱的基础上，探讨了bp网络在matlab环境中的实现，并给出了bp网络建立、训练和仿真过程的编程方法。
( 2 ) it analyzes roundly the solution process and characters of classed forecast and ann model , founds model of ann forecast method and comes true relevant arithmetic with matlab toolbox ( 2 )对传统的预测方法和神经网络预测方法的特点、解决预测问题的思路和步骤进行分析，建立了神经网络预测方法模型，用基于matlab神经网络工具箱的方法实现预测模型的相关算法。
This paper ' s study was emphasized on : 1 . set up bp neural network set up bp network by matlab toolbox . chosen transfer function among every layer , training error and learning rate were included 重点研究内容： ( 1 )建立bp ( backpropagation )神经网络用matlab提供的人工神经网络工具箱建立网络，包括选择各层间的传递函数、确定网络训练误差和学习速率等，为建立定价模型做准备。
First of all , to normalize the load data to [ - 1 , + 1 ] , and then set up the model of load forecast . then training and simulating this model with the neural network toolbox of matlab software , then get all parameters of module after trained 首先将负荷数据归一化，建立负荷预测模型，然后使用matlab软件中的神经网络工具箱对该模型进行仿真训练，计算出训练后的模型各个参数。
In the thesis , how to use the backpropagation training functions in the matlab toolbox to train feedforward neural networks to solve specific problems is explained . besides that , how to design the interface between matlab and delphi is presented 本文在简要介绍了bp神经网络基本原理及其算法的基础上，详细介绍了利用matlab神经网络工具箱进行bp网络模型建立、训练、仿真的编程方法。
Further , the paper introduce the transfer of matlabs . l by vb and the management of the expert rules database and the fault diagnosis of the pre - decomposing rotary kiln system . thirdly , the author analyzes and researches this system in detail 此外，本文还对vb调用matlab5 . 1中神经网络工具箱的方法，应用ado控件实现样本数据库、专家规则库的管理，以及根据水泥预分解窑系统的特点实现的故障诊断功能进行了介绍。
First , features of ncrna were summed up to serve as input for data mining procedures later on . second , statistics toolbox and artificial neural networks toolbox of matlab were used to carry out principal components analysis and artificial neural network training . finally , user prediction was designed in visual c + + while matcom served as interface between matlab and vc to complete the user prediction program 首先利用生物学实验数据总结出ncrna的特征，作为数据挖掘方法的输入；然后在matlab环境下用统计工具箱和神经网络工具箱对输入的特征进行主成分分析和神经网络训练，用训练好的网络去预测ncrna ；最后，为了实现通用性，运用matcom接口与vc实现windows下供用户实际使用的预测程序。
In this study , the model emphasizes particularly on time series of geological entity and at the same time it realizes the integration of the spatial model and the attributive model by integrating complicated spatial and attributive character of forest resources . program is realized by matlab . the ann toolbox of matlab established many tool functions based on ann theory 本项研究中，基于gis的神经网络预测模型主要侧重的是地理实体数量时间结构序列，模型结合森林资源复杂的空间和属性特征，不仅使用了gis关系数据库中的属性时间序列值，同时也使用了一定的空间模型，实现了空间模型与属性模型的有效结z 、口0在程序的实现上采用m八tlab开发环境，其中的神经网络工具箱以人工神经网络理论为基础，构造了网络分析和设计的许多工具函数。
