人工神经网络和遗传算法在微带交指电容器设计中的应用

将神经网络技术(ANN)与遗传算法(GA)相结合对交指电容器(IDC)进行了分析与设计。采用多层感知器神经网络(MLPNN)建立了交指电容器的模型,并利用遗传算法的全局搜索能力根据实际需要优化设计交指电容器的结构。模型训练样本的S参数由时域有限差分(FDTD)方法得到。结果证明该方法具有较高的准确性,并可以节省大量的计算时间。     

Design of electrically loaded wire antennas using geneticalgorithms

A novel antenna design procedure based on genetic algorithm (GA) driven optimization is proposed and applied to the synthesis of wire antennas loaded with lumped components. Loading circuit parameters, locations of the loads along the antenna, as well as matching network parameters, are optimized simultaneously. A computational scheme based on the Sherman-Morrison-Woodbury formula for the fast evaluation of the antenna performance for many distinct loading configurations is developed. The GA iteratively guides a population of randomly selected design candidates toward the optimal solution. The success of the proposed procedure is demonstrated through its application to the design of efficient ultra-broadband antennas and their corresponding matching networks     

基于遗传算法的计算机辅助设计沙发系统

采用结合专业知识编码的遗传算法巡游，并使用人工神经网络逼近适应度函数，建立了一个面向非专业人士的沙发CAD系统，得到了良好的效果。     

Comparison of a genetic algorithm with a simulated annealingalgorithm for the design of an ATM network

The genetic algorithm (GA) and simulated annealing algorithm (SA) are empirically compared for the problem of optimizing the topological design of a network. In addition to the usual problem of optimizing only the placement of links, in this paper the number and placement of concentrators are also decision variables for a class of problems using a real set of concentrators, links, and traffic. The average GA solution cost less than the average SA solution     

Automatic design of frequency sampling filters by hybrid geneticalgorithm techniques

A new method of designing recursive and nonrecursive frequency sampling filters is presented. We investigate the use of a hybrid real-coded genetic algorithm (GA) for optimising transition sample values to give the maximum stopband attenuation. A modification allows the coefficient wordlength to be optimized concurrently, thereby reducing the overall number of design steps and simplifying the design process. The technique is able to consistently optimize filters with up to six transition samples. Designing digital filters is a complex process involving optimization at several discrete design steps. The techniques presented could form the basis for integrating several of the optimizations. Investigations into increasing this integration by using a binary-coded GA to optimize nonlinear phase, quantized coefficient FIR filters are introduced, with an analysis of the difficulty of the problem from a GA perspective     

一种基于GA-BP神经网络结合PCA的LCD显示器光谱特征化模型

为实现LCD显示器的光谱特征化,本文提出一种基于遗传算法优化(Genetic Algorithm,GA)的BP神经网络(GABP)结合PCA(Principal component analysis)的光谱特征化模型。首先对显示器色空间进行子空间划分,同时采用PCA对光谱数据进行降维,接着在各子空间中采用遗传算法对BP神经网络的权值阈值进行优化,建立显示器驱动值与光谱数据之间的神经网络模型,实现了显示器的光谱特征化。实验结果表明子空间划分后,在子空间中进行模型参数的优化有利于模型整体精度的提高,GA的优化有效改善了BP神经网络的极值问题,提高了模型的精度,PCA在不影响模型精度的同时提高了算法的运行效率。由此说明该模型是一种高精度显示器特征化模型。     

Optimal design of reliable network systems in presence of uncertainty

In practice, network designs can be based on multiple choices of redundant configurations, and different available components which can be used to form links. More specifically, the reliability of a network system can be improved through redundancy allocation, or for a fixed network topology, by selection of highly reliable links between node pairs, yet with limited overall budgets, and other constraints as well. The choice of a preferred network system design requires the estimation of its reliability. However, the uncertainty associated with such estimates must also be considered in the decision process. Indeed, network system reliability is generally estimated from estimates of the reliability of lower-level components (nodes & links) affected by uncertainties. The propagation of the estimation uncertainty from the components degrades the accuracy of the system reliability estimation. This paper formulates a multiple-objective optimization approach aimed at maximizing the network reliability estimate, and minimizing its associated variance when component types, with uncertain reliability, and redundancy levels are the decision variables. In the proposed approach, Genetic Algorithms (GA) and Monte Carlo (MC) simulation are effectively combined to identify optimal network designs with respect to the stated objectives. A set of Pareto optimal solutions are obtained so that the decision-makers have the flexibility to choose the compromised solution which best satisfies their risk profiles. Sample networks are solved in the paper using the proposed approach. The results indicate that significantly different designs are obtained when the formulation incorporates estimation uncertainty into the optimal design problem objectives.     

LIBS结合GA-BP神经网络与GA-SVM对塑料分类研究北大核心CSCD

塑料制品回收力度小、重复利用率低,造成环境污染和资源浪费,因此对废旧塑料精确分类是提高塑料回收的关键。本文采用激光诱导击穿光谱(LIBS)技术结合遗传算法优化误差反向传播神经网络(GA-BP)和支持向量机(GA-SVM)对常见的10种塑料进行分类识别。利用LIBS技术对塑料样品进行处理,分别采集每种塑料样品100组光谱。对采集到的原始光谱做滤波和归一化处理,提取光谱中14条主要的特征谱线,分别建立GA-BP神经网络和GA-SVM模型。实验结果表明,GA-BP神经网络对塑料的识别性能优于GA-SVM,其中GA-BP神经网络识别精度为99.25%,原因是GA-SVM利用升维算法实现对数据集的分类,在塑料样品种类多的情况,分类效果不及GA-BP神经网络。因此,利用LIBS技术结合不同的识别算法,可以实现对多种塑料样品的分类,也为研究不同算法对塑料样品分类识别提供研究思路。     

Design of broadband array elements based on neural network and genetic algorithm

Coupled artificial neural network (ANN) and genetic algorithm (GA) models are developed for the design of broadband tapered slot antenna array elements. The ANN is employed to establish the complicated relationships between the key array performance indicator, i.e. active reflection coefficient, and its element parameters. The trained ANN models are combined with the GA to optimise the element parameters for a given operating frequency band without using time-consuming EM simulators. Optimisation results show the developed ANN-GA model can retain the accuracy obtainable from EM simulators and exhibit high computational efficiency.     

一种基于FPGA的PXA270外设时序转换接口设计

为解决ARCNET协议器件COM20020应用于列车通信网络时,与中央控制单元（CCU）处理器PXA270之间时序不匹配的问题．提出一种基于FPGA的PXA270外设时序转换接口设计方案。此外,还在FPGA中增加存储器直接访问（DMA）功能,以减轻PXA270的负担。测试结果表明,该方案成功解决时序匹配问题,增强网络的响应能力。     

Genetic algorithms in engineering electromagnetics

This paper presents a tutorial and overview of genetic algorithms for electromagnetic optimization. Genetic-algorithm (GA) optimizers are robust, stochastic search methods modeled on the concepts of natural selection and evolution. The relationship between traditional optimization techniques and the GA is discussed. Step-by-step implementation aspects of the GA are detailed, through an example with the objective of providing useful guidelines for the potential user. Extensive use is made of sidebars and graphical presentation to facilitate understanding. The tutorial is followed by a discussion of several electromagnetic applications in which the GA has proven useful. The applications discussed include the design of lightweight, broadband microwave absorbers, the reduction of array sidelobes in thinned arrays, the design of shaped-beam antenna arrays, the extraction of natural resonance modes of radar targets from backscattered response data, and the design of broadband patch antennas. Genetic-algorithm optimization is shown to be suitable for optimizing a broad class of problems of interest to the electromagnetic community. A comprehensive list of key references, organized by application category, is also provided     

A meta‐heuristic Bayesian network classification for intrusion detection

Software‐defined networking (SDN) is an innovative network paradigm much in demand today in academics and industry. In this network, the SDN controller must be able to observe and examine traffic flow through the network systems. However, intrusion‐based data packets affect the whole system is a major drawback. To overcome this issue, we propose a Novel Agent Program (NAP) framework for preventing switches from the external compromised attacks. A Meta‐Heuristic Bayesian Network Classification (MHBNC) algorithm for intrusion detection is proposed in this paper. The proposed algorithm follows certain procedures for preprocessing, feature selection, feature optimization, and classification. Normal and anomaly‐based data packets are classified successfully with its improved detection capabilities based on the optimization technique. The simulation results of the proposed ID_MBC (intrusion detection based on meta‐heuristic Bayesian classifier) technique is compared with existing techniques such as the association rule, PSO+GA, and the GA+RVM. The proposed MHBNC classifier performs better than existing methods.     

Design of a wideband microstrip antenna and the use of artificial neural networks in parameter calculation

This paper deals with the design of a multi-slot hole-coupled microstrip antenna on a substrate of 2 mm thickness that gives multi-frequency (wideband) characteristics. The Method of Moments (MoM)-based IE3D software was used to simulate the results for return loss, VSWR, the Smith chart, and the radiation patterns. A tunnel-based artificial neural network (ANN) was also developed to calculate the radiation patterns of the antenna. The radiation patterns were measured experimentally at 10.5 GHz and 12 GHz. The experimental results were in good agreement with the simulated results from IE3D and those of the artificial neural network. A new method of using a genetic algorithm (GA) in an artificial neural network is also discussed. This new method was used to calculate the resonant frequency of a single-shorting-post microstrip antenna. The resonant frequency calculated using the genetic-algorithm-coupled artificial neural network was compared with the analytical and experimental results. The results obtained were in very good agreement with the experimental results.     

遗传算法在FIR滤波器设计——频率抽样法中的应用

本文介绍了遗传算法在FIR滤波器设计——频率抽样法中的应用.用遗传算法确定过渡带样本值,解决了传统方法(查表法)不能保证数据是最优的问题.本文还对标准遗传算法进行了适当的改进.给出了FIR数字低通、带通滤波器设计的两个例子.实验结果说明通过遗传算法设计的FIR滤波器性能较查表法得到了改善.     

Genetic algorithms applied to the scheduling of the hubble space telescope

A prototype system employing a genetic algorithm has been developed in order to determine if this technology can be applied to optimizing schedules of the Hubble Space Telescope. A non-standard knowledge structure is used and appropriate genetic operators have been created. Several different crossover styles (random point selection, evolving points, and smart point selection) are tested and the best GA is compared with a neural network based optimizer. The smart crossover operator produces the best results and the GA system is able to evolve complete schedules using it. The GA is not as time-efficient as the NN system and the NN solutions tend to be better. Work is proposed to create a classifier system that can draw more effectively on the knowledge that is available in the scheduling domain.     

宽带折线栅圆极化器的分析与优化设计

介绍了折线栅圆极器的分析理论和优化设计方法,给出了折线栅等效导纳计算公式的几个待定常系数的确定方法以及在设计频带内的最优常系数值,重新推导了多层折线栅级联网络的传输矩阵的计算公式,修正了文献上公式的错误.基于折线栅圆极化器的分析理论采用遗传算法优化设计了25～35 GHz频带范围内的折线栅圆极化器,并给出了HFSS仿真结果,表明轴比仿真值小于1dB的带宽大于40％,并且插入损耗低,验证了采用的分析和设计方法的有效性.     

Multiobjective optimization design of Yagi-Uda antenna

An optimization method, such as the steepest gradient methods, could not easily obtain globally optimum solutions for devising antenna design parameters that allow the antenna to simultaneously improve multiple performances such as gain, sidelobe level, and input impedance. The genetic algorithm (GA) is suitable for empirically solving optimization problems and is effective in designing an antenna. In particular, this method can solve the multiobjective optimization problem using various Pareto-optimal solutions in an extremely efficient manner. In this paper, the Pareto GA, by which various Pareto-optimal solutions for each objective function (performance) can be obtained in a single trial of a numerical simulation and which enables the selection of parameters in accordance with the design requirement, is applied to the multiobjective optimization design of the Yagi-Uda antenna. The effectiveness of the Pareto GA was demonstrated by comparing the performances obtained by the Pareto GA with those of the previously reported values, which were obtained by the conventional GA, and with the values of the design benchmark reference.     

遗传算法在网络扩充中的应用

通信网在运行的过程中,存在着网络的节点扩充及重组等问题。文章从遗传算法的基本原理出发,分析带有约束条件的通信网设计中的网络扩充问题,较好的解决了经济效益和需求的网络设计问题。     

基于TCM-KNN和遗传算法的网络异常检测技术

提出了一种基于TCM．KNN的网络异常检测新方法，并采用遗传算法选择使用少量高质量的训练样本进行建模，从而有效地对入侵进行检测。大量基于著名的KDD Cup 1999数据集的实验表明：其相对于传统的异常检测方法在保证较高检测率的前提下，有效地降低了误报率；并且，在采用选择后的训练集优化处理后，其性能没有明显的削减，因而相对于传统方法更为适用于现实的网络应用环境。     

Error-rate analysis of polar code designs using genetic algorithm for additive white Gaussian noise channel

A novel scheme for designing polar codes with specific decoding schemes in the additive white Gaussian noise channel is presented in this paper. The code construction strategy is built on the genetic algorithm (GA) where successive evolution of populations (or group of information sets) leads toward fittest candidate to attain finest error-rates. In this work, it is shown that better error-rates can be attained by both successive cancelation list decoding using GA with no cyclic redundancy check (CRC) and belief propagation decoding using GA with no CRC and no list, compared to existing polar decoding schemes. Our proposed polar code design scheme using GA has the ability to attain a target block error rates with the least possible SNR and using no additional CRC by exploiting least belief propagation iterations or lesser successive cancelation list list size with no self-alterations in the decoding algorithm.