基于神经网络PID算法的研究与设计

传统PID的算法模型对于复杂的过程和非线性系统难于建立准确的数据模型,甚至在控制过程中出现不稳定现象.为解决此类问题,按PID控制方式选择神经元学习控制所需的状态变量,将自适应神经元与ID控制算法结合起来,较好地解决了交流变频调速系统负载扰动、时滞现象及非线性控制的问题.通过仿真实验比较传统PID和NN PID算法,说明NN PID具有较好的鲁棒性.     

基于神经网络的半导体扩散炉温度控制研究

针对半导体扩散炉温度控制问题,提出神经网络控制方法.神经网络采用3-3-3结构.仿真表明,此控制方法是可行的,具有良好的控制品质.     

RBF网络参数确定方法的研究

针对RBF网络结构参数确定问题,介绍了几种RBF网络结构参数确定方法,如基于遗传算法的训练方法和基于粒子群优化算法的训练方法等.实践表明,此RBF网络结构参数确定方法具有实用、高效等特点.     

ERP系统实施效果的BP神经网络评价研究

神经网络具有自学习、自适应能力和容错性强等特点,本文应用BP神经网络模型建立综合评价系统,应用人工神经网络方法对ERP系统实施效果进行评价作了一定的探索。     

试论企业竞争战略之核心竞争力

自美国学者普拉哈拉德与哈默尔提出企业核心竞争力的概念以来,日益引起了理论界与企业界的广泛关注。本文首次系统地对企业核心竞争力的起源、现状、流派、理论发展和本质进行了分析,并且从市场表现、技术实力或服务水平以及内部管理等三个层面详细分析了企业核心竞争力的内涵,同时提出了一种评价企业核心竞争力的方法和指标体系。然后依据该评价指标体系的指标特性,选择神经网络作为评价方法。最后根据企业核心竞争力的评价结果进行了初步策略应用探讨,具有切实可行的实际操作和指导意义。     

基于递阶遗传算法的神经网络预测控制(英文)

针对具有时延非线性系统提出一种神经网络预测控制．相应的预测模型通过递阶遗传算法离线训练数据并实时多步递推得到;性能函数中引入模型误差校正以克服模型失配及干扰等对系统造成的影响．仿真结果表明该系统具有良好动态响应及鲁棒性．     

Application of BP NN and RBF NN in Modeling Activated Sludge System

Based on the operation data from a certain wastewater treatment plant(WWTP) in northeast China,the models of back propagation neural network ( BP NN ) and radial basis function neural network ( RBF NN ) have been designed respectively and the ability of convergence and generalization has been analyzed separately.As for BP NN, the effects of numbers of layers and nodes have been studied ; as for RBF NN, the influences of the number of nodes and the RBF‘s width have been studied. It is concluded that BP NN has converged much slowly in comparison with RBF NN. The conclusion that the RBF NN is suitable for modeling activated sludge system has been drawn. An automatically optimum design program for RBF NN has been developed, through which the RBF NN model of traditional activated sludge system has been established.     

模糊PID控制及其进展

模糊控制(Fuzzy Control)和PID控制技术相互取长补短、相互融合而形成的模糊控制PID技术,可显著提高控制系统的品质,并已在实际系统的控制中得到了应用和验证。文章首证述了近年来出现的几种模糊PID控制器的结构和原理,并对它们各自的优点和存在的问题进行了分析和总结,对模糊PID控制技术进行了展望。     

珠江三角洲河口区适用的两种水位过程预报方法

简要介绍了非线性的神经网络和分量级的多元线性回归模型 ,并采用这两种方法对广东省珠江三角洲河口区水位过程进行预报分析     

Multi-objective meta level soft computing-based evolutionary structural design

Evolutionary structural design has been the topic of much recent research; however, such designs are usually hampered by the time-consuming stage of prototype evaluations using standard finite element analysis (FEA). Replacing the time-consuming FEA by neural network approximations may be a computationally efficient alternative, but the error in such approximation may misguide the optimization procedure. In this paper, a multi-objective meta-level (MOML) soft computing-based evolutionary scheme is proposed that aims to strike a balance between accuracy vs. computational efficiency and exploration vs. exploitation. The neural network (NN) is used here as a pre-filter when fitness is estimated to be of lesser significance while the standard FEA is used for solutions that may be optimal in their current population. Furthermore, a fuzzy controller updates parameters of the genetic algorithm (GA) in order to balance exploitation vs. exploration in the search process, and the multi-objective GA optimizes parameters of the membership functions in the fuzzy controller. The algorithm is first optimized on two benchmark problems, i.e. a 2-D Truss frame and an airplane wing. General applicability of the resulting optimization algorithm is then tested on two other benchmark problems, i.e. a 3-layer composite beam and a piezoelectric bimorph beam. Performance of the proposed algorithm is compared with several other competing algorithms, i.e. a fuzzy-GA-NN, a GA-NN, as well as a simple GA that only uses only FEA, in terms of both computational efficiency and accuracy. Statistical analysis indicates the superiority as well as robustness of the above approach as compared with the other optimization algorithms. Specifically, the proposed approach finds better structural designs more consistently while being computationally more efficient.