基于进化规划的神经网络群的设计方法

提出一种基于进化规划的神经网络群的自动设计方法．该方法不仅使得神经网络群中的个体网络倾向于完成不同的子任务，同时各神经网络个体在进化过程中不断寻找最好的协作关系，而且神经网络群的规模和结构不需预先设定．仿真试验表明，该算法是有效的。     

基于自适应进化神经网络算法的入侵检测

针对目前多数入侵检测系统的低检测率问题,提出一种自适应进化神经网络算法AENNA。基于遗传算法和BP神经网络算法,利用模拟退火算法的概率突跳和局部搜索强的特性对遗传算法进行改进,采用双种群策略的遗传进化规则实现BP神经网络权值和结构的双重优化;通过对遗传算法的交叉算子与变异算子的改进,设计一种自适应的神经网络训练方法。实验结果表明,基于AENNA的入侵检测方法能够有效提高系统的检测率并降低误报率。     

基于知识模型的改进遗传算法

针对遗传算法个体进化缺乏明确导向的缺点,该文提出了一种基于知识模型的改进遗传算法,将遗传算法和神经网络有效结合起来,利用神经网络的学习功能。构造知识模型,用来引导群体中某些个体的进化。模拟实验验证了该算法的有效性。     

基于协同进化微粒群算法的神经网络自适应噪声消除系统

在分析前向神经网络结构的基础上,定义了一个与随机数对应的布尔向量,实现了前向神经网络的网络结构与权值联合编码;将网络结构参数作为协同进化微粒群算法子群的划分标志,构造了一种用于神经网络进化设计的协同进化微粒群算法,实现了神经网络的结构和权值协同自适应进化设计,应用于神经网络噪声消除系统,取得了比较好的效果。     

一种基于进化算法的BP神经网络优化方法及应用

针对BP网络的不足,分析了一般进化算法在神经网络结构优化过程中存在的问题,根据物种内优生优育原则和物种间相互竞争、相互学习的生物学原理,提出了一种新的基于进化算法的神经网络优化方法。该方法不但有效弥补了BP神经网络在网络结构、权值选择上的随机性缺陷,缩小了神经网络结构的解搜索空间,加快了BP网络的收敛速度,进而提高了搜索效率,而且还起到对网络的结构和权值进行同时进化的作用。实验结果表明该方法取得了良好的效果。     

一种基于进化算法的BP神经网络优化方法及应用

针对BP网络的不足,分析了一般进化算法在神经网络结构优化过程中存在的问题,根据物种内优生优育原则和物种间相互竞争、相互学习的生物学原理,提出了一种新的基于进化算法的神经网络优化方法。该方法不但有效弥补了BP神经网络在网络结构、权值选择上的随机性缺陷,缩小了神经网络结构的解搜索空间,加快了BP网络的收敛速度,进而提高了搜索效率,而且还起到对网络的结构和权值进行同时进化的作用。实验结果表明该方法取得了良好的效果。     

利用进化规划设计人工神经网络

近年来,人们开发了许多的神经网络自动设计方法,其中进化算法与神经网络的结合最引人注目,形成一类进化人工神经网络。对用遗传算法实现神经网络的进化进行研究的时间最早,并得到广泛的评论,而最近越来越多的研究支持利用进化规划设计神经网络是一种更优的方法。该文讨论了这一领域的研究进展并阐明了进一步的研究方向。     

进化神经网络研究综述

进化算法(EAs)与神经网络(NN)的结合已形成了一个新的领域一进化神经网络,在神经网络的研究中举足轻重。本文通过讨论和总结进化神经网络中的关键技术和现状，概述了其设计与构造的趋势。所讨论的是：(1)进化神经网络的研究方法；(2)进化模型；(3)应用实例及关键技术；(4)研究方向。     

基于DMDE算法的前向神经网络设计

针对神经网络结构与参数并行优化问题,提出一种基于动态多群体差分进化算法的前向神经网络设计方法。采用分层递阶结构原理构造算法个体,根据控制基因信息将个体分成不同的动态群体。通过对个体进行重构,实现进化过程中个体信息的充分交换与共享。设计基于群体适应度的控制基因更新方法来优化网络拓扑结构,克服结构优化的盲目与低效问题。将所设计的神经网络应用于大包线飞行控制律参数拟合中。仿真结果表明,该算法能快速有效地确定神经网络的结构和权值,所优化的网络在调参控制中具有较好的泛化能力。     

进化神经网络研究进展

进化神经网络是将进化算法应用于神经网络的构造、学习而得到的神经网络，具有很强的鲁棒适应性。综述了进化神经网络方法及其应用研究新进展，对研究中出现的一些问题进行了讨论与展望。     

基于模糊进化规划和分层方法的神经网络设计方法

本文提出一种模糊进化规划，用于前向神经网络的设计．该方法通过对神经元的部分解群体的进化，缩短了个体的编码长度，显著地减轻了计算量，同时这种方法不但能够在很大程度上简化适应值的计算，更重要的是能够降低适应值空间的复杂性，从而能够加速进化算法收敛到全局最优点．仿真结果显示，本文提出的算法能够有效抑制进化规划算法初期收敛的发生，有效地提高多层前向神经网络收敛精度，并可获得更为简洁的网络结构．     

Evolutionary design and behavior analysis of neuromodulatory neural networks for mobile robots control

Evolutionary Robotics (ER) is one of promising approaches to design robot controllers which essentially have complicated and/or complex properties. In most ER research, the sensory–motor mappings of robots are represented as artificial neural networks, and their connection weights (and sometimes the structure of the networks) can be optimized in the parameter spaces by using evolutionary computation. However, generally, the evolved neural controllers could be fragile in unexperienced environments, especially in real worlds, because the evolutionary optimization processes would be executed in idealized simulators. This is known as the gap problem between the simulated and real worlds. To overcome this, the author focused on evolving an on-line learning ability instead of weight parameters in a simulated environment. According to recent biological findings, actually, the kinds of on-line adaptation abilities can be found in real nervous systems of insects and crustaceans, and it is also known that a variety of neuromodulators (NMs) play crucial roles to regulate the network characteristics (i.e. activating/blocking/changing of synaptic connections). Based on this, a neuromodulatory neural network model was proposed and it was utilized as a mobile robot controller. In the paper, the detail behavior analysis of the evolved neuromodulatory neural network is also discussed.     

进化神经网络在机床工具损耗预测中的应用

设计了一个基于实数编码的改进进化算法优化神经网络的连接权和网络结构.该算法 可以根据种群停止进化代数自适应调节变异率、根据个体适应度调节变异量.加工实验表明采用 进化神经网络可以较准确预测出电火花铣削加工工具损耗,所提出的进化算法是有效的,预测结 果较标准BP神经网络高.该预测模型为电火花铣削加工工具在线自动补偿打下基础.     

Evolutionary Fuzzy ARTMAP Neural Networks and their Applications to Fault Detection and Diagnosis

In this paper, two mutation-based evolving artificial neural networks, which are based on the Fuzzy ARTMAP (FAM) network and evolutionary programming, are proposed. The networks utilize the knowledge base extracted from a set of data to perform search and adaptation. The performances of the two networks are assessed using benchmark problems, with the results analyzed and discussed. The effects of the network parameters are evaluated through a parametric study. The applicability of the networks is also demonstrated using a real fault detection and diagnosis task in a power generation plant. The experimental results consistently indicate the usefulness of the proposed evolutionary FAM-based networks in yielding good classification performances with parsimonious network structures.     

一种基于免疫调节和共生进化的神经网络优化设计方法

利用共生进化原理设计人工神经网络,创造性地融入了免疫调节原理中的浓度抑制调节机制以保持个体的多样性,提出了基于免疫调节的共生进化网络设计方法,通过对神经元群体而不是神经网络群体进行进化设计,显著地减轻了计算量,同时利用生物免疫原理中的浓度机制和个体多样性保持策略进行免疫调节,有效地克服了未成熟收敛现象,提高了群体的多样性,从而加快优化设计速度,实验表明表明该方法可高效、准确地设计鲁棒性很强的神经     

Computational intelligence in management of ATM networks

Designing effective control strategies for asynchronous transfer mode (ATM) networks is known to be difficult because of the complexity of the structure of networks, nature of the services supported, and variety of dynamic parameters involved. Additionally, the uncertainties involved in identification of the network parameters cause analytical modeling of ATM networks to be almost impossible. This renders the application of classical control system design methods (which rely on the availability of these models) to the problem even harder. Consequently, a number of researchers are looking at alternative non-analytical control system design and modeling techniques that have the ability to cope with these difficulties to devise effective, robust ATM network management schemes. Those schemes employ artificial neural networks, fuzzy systems and design methods based on evolutionary computation. In this survey, the current state of ATM network management research employing these techniques as reported in the technical literature is summarized. The salient features of the methods employed are reviewed.     

基于郭涛算法的演化神经网络

提出了一种新的演化神经网络算法GTEANN,该算法基于高效的郭涛算法,同时完成在网络结构空间和权值空间的搜索,以实现前馈神经网络的自动化设计。本方法采用的编码方案直观有效,基于该编码表示,神经网络的学习过程是一个复杂的混合整实数非线性规划问题,例如杂交操作包括网络的同构和规整处理。初步实验结果表明该方法收敛,能够达到根据训练样本自动优化设计多层前馈神经网络的目的。     

COVNET: a cooperative coevolutionary model for evolving artificial neural networks

This paper presents COVNET, a new cooperative coevolutionary model for evolving artificial neural networks. This model is based on the idea of coevolving subnetworks that must cooperate to form a solution for a specific problem, instead of evolving complete networks. The combination of this subnetworks is part of a coevolutionary process. The best combinations of subnetworks must be evolved together with the coevolution of the subnetworks. Several subpopulations of subnetworks coevolve cooperatively and genetically isolated. The individual of every subpopulation are combined to form whole networks. This is a different approach from most current models of evolutionary neural networks which try to develop whole networks. COVNET places as few restrictions as possible over the network structure, allowing the model to reach a wide variety of architectures during the evolution and to be easily extensible to other kind of neural networks. The performance of the model in solving three real problems of classification is compared with a modular network, the adaptive mixture of experts and with the results presented in the bibliography. COVNET has shown better generalization and produced smaller networks than the adaptive mixture of experts and has also achieved results, at least, comparable with the results in the bibliography.