小波分析在控制中的应用概况

小波分析是80年代发展起来的一门新兴科学。在简单介绍小波分析的发展和基本原理的基础上，综述了小波分析和小波网络在控制领域中的主要应用情况，分析了小波分析现存的几个问题，并对它的发展作了展望。     

四容水箱的小波神经网络广义预测控制

针对四容水箱系统的多变量、大时滞、非线性及强耦合等特性,采用了小波神经网络广义预测控制（WNNGPC）策略。利用小波神经网络良好的函数逼近能力,对系统被控对象进行辨识,得到控制系统的预测模型,再结合广义预测控制良好的控制性能,达到对四容水箱系统的稳定控制。在系统辨识的过程中,采用的是改进的BP学习算法,这一算法能够快速平稳地修正网络权值和阈值,使预测输出平滑地趋近期望输出。在解决系统的耦合问题上,利用了模糊控制的通用逼近性,设计了模糊前馈补偿解耦。基于模糊补偿解耦的WNNGPC对四容水箱进行控制实验,并对比分析实验结果。结果表明,这一控制策略对四容水箱进行控制时取得了较好的控制效果。     

一种基于免疫和Hopfield神经网络的多峰值优化算法

分析了免疫算法和Hopfield神经网络的优缺点,提出了一种解决多峰值函数优化问题的混合算法。Hopfield神经网络易于硬件实现,具有简单、快速的优点,但是对初始值具有依赖性以及容易陷入局部极值。免疫算法具有识别多样性的特点,但搜索效率和精度不高。将两算法结合起来,优势互补。首先用免疫算法寻优,然后对所得具有全局多样性的解进行聚类分析,所得聚类中心作为Hopfield神经网络的初始搜索点,最后利用Hopfield神经网络逐个寻优。实验表明,该算法是一种有效的求解多峰函数优化问题的方法,与免疫算法相比,搜索效率和精度都较高。     

基于Hopfield神经网络的WSN分布式拓扑

提出一种基于Hopfield神经网络模型的传感器网络的分布式广播算法。在已有网络拓扑的基础上对其数据获取方式进行改进。用优化的Hopfield神经网络模型在各簇中分别从广播源点开始遍历所有传感节点,并返回广播源点的最优链路。利用Hopfield神经网络收敛速率快、通信路径最优,且易于硬件电路实现的特点,形成了能量消耗较少、延时较小的WSN网络,它是一种能量高效的网络。     

一种基于模糊神经网络的语音端点检测方法

为了提高语音端点检测的适应性和鲁棒性,提出一种基于小波分析和模糊神经网络的语音端点检测方法。利用小波变换得到语音信号的特征量,以这些特征量为模糊神经网络的输入进行运算,判断出该信号的类别。介绍了信号特征量的提取以及模糊神经网络的模型、学习算法等。实验表明,与传统的检测方法相比,所提出的方法有较好的适应性和鲁棒性,对不同信噪比的信号都有较好的检测能力。     

基于小波变换多分辨率特征提取的模拟电路故障诊断方法研究

提出一种基于小波变换多分辨率特征提取的模拟电路故障诊断的方法。该方法先对采样后的故障信号进行小波分解,提取各频段系数作为特征向量输入到神经网络进行训练。通过带通滤波器电路诊断的实例,阐述该方法的具体实现,验证该方法可以有效地简化神经网络结构和减少它的训练时间,快速高效地进行模拟电路故障的诊断和定位。     

基于小波多尺度变换的光纤陀螺振动误差分析与补偿

针对振动环境对光纤陀螺性能的影响,对某型号的光纤陀螺进行了线振动实验并对实验结果进行了Allan方差分析。利用小波多尺度变换提取了光纤陀螺误差模型中的各误差项,分析并验证了零漂及噪声误差与Allan方差分析误差系数中的量化噪声、角度随机游走以及零偏误差与误差系数中的零偏稳定性、速率随机游走、速率斜坡之间的对应关系。随后利用RBF神经网络对小波多尺度分析提取的零偏误差建立模型并进行了补偿。仿真结果表明,本文提出的方法有效减小了振动环境下各误差项对光纤陀螺性能的影响,Allan方差分析结果中的五个误差系数均有较大下降,其中两项误差系数下降了一个数量级及以上,极大提高了光纤陀螺在振动环境下的输出精度,对光纤陀螺在振动环境下的误差研究具有重要指导意义。     

A self-organizing neural network approach for the design of cellular manufacturing systems

The crux problem of group technology (GT) is the identification of part families requiring similar manufacturing processes and the rearrangement of machines to minimize the number of parts that visit more than one machine cell. This paper presents an improved method for part family formation, machine cell identification, bottleneck machine detection and the natural cluster generation using a self-organizing neural network. In addition, the generalization ability of the neural network makes it possible to assign the new parts to the existing machine cells without repeating the entire computational process. A computer program is developed to illustrate the effectiveness of this heuristic method by comparing it with the optimal technique for large-scale problems.     

Line flow contingency selection and ranking using cascade neural network

Line flow or real-power contingency selection and ranking is performed to choose the contingencies that cause the worst overloading problems. In this paper, a cascade neural network-based approach is proposed for fast line flow contingency selection and ranking. The developed cascade neural network is a combination of a filter module and a ranking module. All the contingency cases are applied to the filter module, which is trained to classify them either in critical contingency class or in non-critical contingency class using a modified BP algorithm. The screened critical contingencies are passed to the ranking module (four-layered feed-forward artificial neural network (ANN)) for their further ranking. Effectiveness of the proposed ANN-based method is demonstrated by applying it for contingency screening and ranking at different loading conditions for IEEE 14-bus system. Once trained, the cascade neural network gives fast and accurate screening and ranking for unknown patterns and is found to be suitable for on-line applications at energy management centre.     

煤与瓦斯突出的PCA-BP神经网络预测模型研究

通过主成分分析法对煤与瓦斯突出的主要影响因素进行主成分提取,选取贡献率大于85%的3个主成分来代替原来的7个影响因素,以此来确定BP神经网络的输入参数为3个。根据煤与瓦斯突出的类型,建立了煤与瓦斯突出预测的PCA-BP神经网络模型。选用典型突出矿井的煤与瓦斯突出实例作为学习样本,对PCA-BP网络进行训练。以云南某煤矿的煤与瓦斯突出实例作为预测样本,仿真结果表明PCA-BP神经网络模型性能优于传统BP神经网络,能够满足煤与瓦斯突出预测的要求。     

基于小波包分析和RBF神经网络的ERT系统流型辨识

两相流体具有复杂性的流动特性,流型的准确辨识是两相流参数准确测量的基础,流型的在线智能辨识成是两相流研究的重点内容之一。以ERT系统和油/水两相流的流型为研究基础,采用小波包分析方法对测量数据进行特征提取,然后以提取后的特征数据作为RBF神经网络的输入,对网络进行建模和仿真。通过实验仿真分析,该方法对流型辨识非常适用,并有效达到流型辨识的目的。     

An analysis of the interface between evolutionary algorithm operators and problem features for water resources problems. A case study in water distribution network design

Evolutionary Algorithms (EAs) have been widely employed to solve water resources problems for nearly two decades with much success. However, recent research in hyperheuristics has raised the possibility of developing optimisers that adapt to the characteristics of the problem being solved. In order to select appropriate operators for such optimisers it is necessary to first understand the interaction between operator and problem. This paper explores the concept of EA operator behaviour in real world applications through the empirical study of performance using water distribution networks (WDN) as a case study. Artificial networks are created to embody specific WDN features which are then used to evaluate the impact of network features on operator performance. The method extracts key attributes of the problem which are encapsulated in the natural features of a WDN, such as topologies and assets, on which different EA operators can be tested. The method is demonstrated using small exemplar networks designed specifically so that they isolate individual features. A set of operators are tested on these artificial networks and their behaviour characterised. This process provides a systematic and quantitative approach to establishing detailed information about an algorithm's suitability to optimise certain types of problem. The experiment is then repeated on real-world inspired networks and the results are shown to fit with the expected results.