Synthesis of a new meso／microporous composite molecular sieve of MCM-41／mordenite

An MCM-41/mordenite composite with twofold porous structure and stepwise-distributed acidity was prepared for the first time by using zeolite mordenite as the silica-alumina source. The composite molecular sieve has been investigated and compared with a mechanical mixture of mordenite and MCM-41 for their structure, acidity and catalytic activity by means of XRD, N2 adsorption and desorption, HRTEM, DTG, NH3-TPD and catalytic reaction. The characterization results show that the structure and property of the composite molecular sieve are quite different from those of the mechanical mixture, which might be ascribed to the incorporation of secondary building units characteristic of zeolite mordenite into the mesoporous walls of the composite which gives rise to the thicker mesoporous walls, the higher hydrothermal stability and more strong acid sites. Furthermore, the new strategy could be used as a new general method for the preparation of catalysts for the reaction system with multifold large molecules, and the results were well confirmed by the dealkylation of C10^ aromatic hydrocarbon.     

新型力觉径向基神经网络整定PD控制算法研究

结合双向液压伺服系统的特点，通过对国内外的双向伺服控制系统分析，提出一种以主从手之间的力偏差作为从动端控制信号，位置偏差形成主手控制量的新型力反馈双向伺服控制系统，同时采用了RBF神经网络整定PD控制算法。通过仿真实验结果表明，该算法对新型力觉双向伺服控制系统具有控制简单、收敛速度快、实时性好的特点，进而改善了系统的动态性能，具有较强的鲁棒性、自适应性和快速性。     

MATLAB在人机界面评价中的应用

利用MATLAB神经网络工具箱中的RBF神经网络函数设计出人机交互的人机界面评价平台．采用MATLAB的数据库编程技术实现学习样本的存储和积累．为了使用户可以脱离庞大的MATLAB环境而运行MATLAB程序，应用MATLAB Runtime Server技术实现了软件的独立运行．最后通过对3种指针表显示设计方案的评价，表明该人机界面评价软件使用简便，评价结果合理、可靠．     

基于PCA的RBF神经网络预测方法研究

主成分分析(PCA)法可以提取样本集的主成分,实现样本的最优压缩,从而降低样本的维数。针对用RBF神经网络进行多因素时间序列预测时样本特征指标过多的问题,提出用统计理论的PCA方法对数据进行预处理,再选出几个主成分作为神经网络的输入节点.仿真实验表明,基于PCA的RBF神经网络模型在拟合预测中与一般的RBF神经网络模型相比有较好效果,简化了网络结构,改善了预测精度.     

基于高斯RBF神经网络的可伸缩机械臂系统动态建模与仿真

设计了一种新的多模块机器人机械臂系统,该机械臂的每个模块具有可伸缩杆和旋转节两部分.为了进行有效控制,给出了系统的解析公式和机械臂自适应控制的相关技术.使用高斯函数的RBF神经网络对系统的动态方程参数进行了估计,使用李亚普诺夫稳定性分析确定神经网络权重的自适应修正规则,并给出了相应的自适应控制器模型.对该机械臂系统的控制进行了计算机仿真分析,结果表明:该方法在响应时间和控制精度方面都有提高,并且在有负载等非线性事件突然干扰的情况下亦能进行有效控制.     

一类非线性滞后系统的自整定PID控制

针对采用传统PID控制一类非线性滞后系统,难以获得满意的控制效果,提出基于RBF神经网络的PID控制参数自整定的方法.利用具有在线能力的最近零聚类学习算法,训练RBF神经网络,从而自适应调整系统的控制参数.仿真结果证明了,该控制策略不仅能使非线性滞后系统具有良好的动态跟踪性能,而且具有很好的抗干扰能力.     

基于RBF网络的混沌动力系统辨识

提出用RBF神经网络对混沌动力系统进行辨识，设计了一个三层RBF网络结构，仿真实验说明了RBF网络用于学习混沌动力系统时的基本性质。用辨识模型重建吸引子方法定性地评价辨识模型，通过计算辨识模型的Lyapunov指数定量地评价辨识模型的性能，同时推导了RBF网络模型Lyapunov指数的计算公式。仿真结果表明，该辨识模型能很好地逼近原混沌动力系统，准确地体现原混沌系统的动力学特性。     

基于软测量的钢坯表面温度预报模型研究

本文以步进式加热炉中钢坯温度分布为应用背景，提出了利用径向基（RBF）神经网络来实现钢坯表面温度预报的软测量模型，并且对典型工况进行了仿真，结果表明，测量精度完全符合工业应用要求。     

Study of CNG／diesel dual fuel engine＇s emissions by means of RBF neural network

Great efforts have been made to resolve the serious environmental pollution and inevitable declining of energy resources. A review of Chinese fuel reserves and engine technology showed that compressed natural gas (CNG)/diesel dual fuel engine (DFE) was one of the best solutions for the above problems at present. In order to study and improve the emission performance of CNG/diesel DFE, an emission model for DFE based on radial basis function (RBF) neural network was developed which was a black-box input-output training data model not require priori knowledge. The RBF centers and the connected weights could be selected automatically according to the distribution of the training data in input-output space and the given approximating error. Studies showed that the predicted results accorded well with the experimental data over a large range of operating conditions from low load to high load. The developed emissions model based on the RBF neural network could be used to successfully predict and optimize the emissions performance of DFE. And the effect of the DFE main performance parameters, such as rotation speed, load, pilot quantity and injection timing, were also predicted by means of this model. In resum6, an emission prediction model for CNG/diesel DFE based on RBF neural network was built for analyzing the effect of the main performance parameters on the CO, NOx emissions of DFE. The predicted results agreed quite well with the traditional emissions model, which indicated that the model had certain application value, although it still has some limitations, because of its high dependence on the quantity of the experimental sample data.     

基于交叉验证的逐步回归结合RBF神经网络在QS AR中的应用

提出将交叉验证方法和逐步回归方法相结合实现建模变量的筛选,与RBF网络相结合用于建立定量构效关(QSAR)研究的数学模型,并用于不对称有机磷的QSAR研究.结果表明该方法是有效的.