强流束晕-混沌的外部磁场开关控制

研究了强流离子束在周期聚焦磁场通道中束晕-混沌的外部磁场开关参数控制方法. 将该方法应用在多粒子模型中，实现了对5种不同初始分布质子束的束晕-混沌的有效控制，得到了消除束晕及其再生现象的理想结果.在强流加速器系统中，由于外部磁场是可测和可调 的物理量，因此该控制方法有利于实验研究，可为强流质子加速器中周期聚焦磁场的设计和实验提供参考. 关键词： 强流离子束 周期聚焦磁场通道 束晕_混沌 混沌控制 开 关控制     

强流束晕-混沌的外部磁场自适应控制

 研究了强流质子束在周期聚焦磁场通道中束晕 混沌的外部磁场自适应控制方法,给出了磁场控制方程。将该方法应用在多粒子模型中,实现了对4种不同初始分布质子束的束晕 混沌的有效控制,得到了消除束晕及其再生现象的理想结果。在强流加速器系统中,由于外部磁场是可测和可调的物理量,因此该控制方法有利于实验研究,可为强流质子加速器中周期聚焦磁场的设计和实验提供参考。     

强流束晕-混沌的外部磁场滑模变结构控制

 研究了周期性聚焦磁场通道中,束晕-混沌的外部磁场滑模变结构控制方法。通过选择适当的滑模函数,根据李雅普洛夫稳定性条件,推导出外部磁场的滑模变结构控制器。模拟结果表明,在控制条件下,混沌变化的束包络半径能被控制到匹配半径。将该方法应用在多粒子模型中,实施每隔一个磁场周期就调节一次磁场幅度的控制策略,可实现对初始分布为K-V分布离子束的束晕-混沌的有效控制,束平均发射度降低了80%左右,束晕强度因子变为0,束流质量得到了很好的改善,消除了束晕及其再生现象。由于外部磁场是可测和可调的物理量,控制器简单且利于实现,研究结果可为强流离子加速器中周期性聚焦磁场的设计与试验提供参考。     

非线性共振及束晕-混沌的场强参数特性

通过计算和分析周期聚焦磁场强度和表征束自生场强度的束导流系数的变化对强流粒子束运动特征的影响,系统研究了强流粒子束的非线性共振和束晕现象。采用庞加莱截面技术对束核包络振荡及其单粒子运动轨迹进行了数值模拟,清楚地展示了不同情况下束核包络非线性振荡以及对应的试验粒子空间分布。结果表明:束核自生场使得束核包络发生非线性振荡,随着束核自生场和聚焦磁场的增加,伴随着束核共振岛的出现,单粒子空间分布出现越来越多的束晕粒子。     

均匀聚焦磁场中束晕-混沌的孤子控制

采用粒子.束核模型,对均匀聚焦磁场中满足K-V分布的离子束进行模拟研究.观察到束晕.混沌现象,基于束晕.混沌的非线性控制策略,提出控制束晕.混沌的孤子函数控制器,并给出具体的实施方案.模拟研究表明,运用这种方法可以消除束晕及其再生现象,达到对束晕.混沌的有效控制.     

束晕-混沌的复杂性理论与控制方法及其应用前景

本文系统论述涉及强流加速器等强流离子束装置中产生的束晕 混沌的复杂性理论与控制方法及其应用前景。强流离子束在核材料生产与增殖、洁净核能、放射性废物嬗变、放射性药物生产、重离子聚变、高能物理、核科学与工程、国防与民用工业和医疗等许多方面都有极其重要的应用潜力和诱人的发展前景。尤其是,近年来强流加速器驱动的放射性洁净核能系统是国内外关注的热门课题,因为它比常规核电更安全、更干净、更便宜。但是,强流离子束形成的束晕 混沌的复杂性现象已引起了国内外广泛关注,需要加以抑制、控制和消除这类现象,解决这一难题已经成为强流离子束应用中的关键问题之一。目前不仅必须深入研究这类束晕 混沌的复杂特性及其产生的物理机制,而且需要研究如何实现对束晕 混沌的有效控制,并寻求和发展其新理论、新方法和新技术。这就向强流离子束物理和非线性-复杂性科学及其技术提出了一系列极富挑战性的新课题。本文结合国内外的研究概况,根据我们多年来的研究成果,特别是我们首创性地提出了一些束晕 混沌的有效控制方法,它们包括:非线性反馈控制 法,小波反馈控制法,变结构控制法,延迟反馈控制法,参数自适应控制法等,进行重点的介绍。对上述课题当前的主要进展及相关问题进行系统的总结和比较全面综述的评论。最后 ,指出该领域今后的研究方向 ,以推动这个崭新领域的深入研究和应用发展。     

周期聚焦磁场中束晕-混沌的简单函数控制

本文采用粒子-束核模型,基于束晕-混沌的非线性控制策略,对周期性聚焦磁场中满足K—V分布的离子束进行模拟研究,提出了控制其束晕-混沌的简单函数控制器,并给出具体的实施方案．数值模拟研究表明,运用这种方法可以消除束晕及其再生现象,达到对束晕-混沌的有效控制．     

以小区域内的离子数为控制信息实现束晕的有效控制

利用匹配半径外的某些固定区域内的离子数目提供控制信息,采用对数函数控制器对强流离子束进行束晕－混沌控制的数值模拟研究. 结果显示,该方法能够有效地抑制五种不同初始分布的离子束的束晕再生现象. 该方法由于控制信息的探测区域小且能固定,在实验上便于实施. 关键词： 束晕 控制 局部区域 信息     

束晕-混沌的复杂性理论与控制方法及其应用前景

本文系统论述涉及强流加速器等强流离子束装置中产生的束晕-混沌的复杂性理论与控制方法及其应用前景。强流离子束在核材料生产与增殖、洁净核能、放射性废物嬗变、放射性药物生产、重离子聚变、高能物理、核科学与工程、国防与民用工业和医疗等许多方面都有极其重要的应用潜力和诱人的发展前景。尤其是，近年来强流加速器驱动的放射性洁净核能系统是国内外关注的热门课题，因为它比常规核电更安全、更干净、更便宜。但是，强流离子束形成的束晕-混沌的复杂性现象已引起了国内外广泛关注，需要加以抑制、控制和消除这类现象，解决这一难题已经成为强流离子束应用中的关键问题之一。目前不仅必须深入研究这类束晕-混沌的复杂特性及其产生的物理机制，而且需要研究如何实现对束晕-混沌的有效控制，并寻求和发展其新理论、新方法和新技术。这就向强流离子束物理和非线性-复杂性科学及其技术提出了一系列极富挑战性的新课题。本文结合国内外的研究概况，根据我们多年来的研究成果，特别是我们首创性地提出了一些束晕-混沌的有效控制方法，它们包括：非线性反馈控制法，小波反馈控制法，变结构控制法，延迟反馈控制法，参数自适应控制法等，进行重点的介绍。对上述课题当前的主要进展及相关问题进行系统的总结和比较全面综述的评论。最后，指出该领域今后的研究方向，以推动这个崭新领域的深入研究和应用发展。     

束晕-混沌控制中的粒子跟踪模拟研究

运用PIC程序研究了强流离子束中粒子的横向运动，发现了不加控制时束晕粒子并非一直处于晕区和施加非线性控制后粒子的横向运动被限制在一定的范围内形成环状，以及均方根半径的变化近似成为周期运动等性质.根据观察到的均方根半径及其变化率的规律性，提出了一种新的自适应控制器.用该控制器不仅能在很短的时间内完全控制住束晕，而且不需要对增益因子进行精确计算，也能在系统参数改变的情况下取得较好的控制效果. 关键词： 束晕_混沌 粒子跟踪 数值模拟 自适应控制     

Control of Halo-Chaos in Beam Transport Network via Neural Network Adaptation with Time-Delayed Feedback

Subject of the halo-chaos control in beam transport networks (channels) has become a key concerned issue for many important applications of high-current proton beam since 1990'. In this paper, the magnetic field adaptive control based on the neural network with time-delayed feedback is proposed for suppressing beam halo-chaos in the beam transport network with periodic focusing channels. The envelope radius of high-current proton beam is controlled to reach the matched beam radius by suitably selecting the control structure and parameter of the neural network, adjusting the delayed-time and control coefficient of the neural network.     

Self-adaptation algorithm for neural networks with search behavior

In the present paper, a training algorithm with nontraditional capabilities and self-adaptation is suggested for a new generation of neural networks with search behavior. The results obtained open up new opportunities for progress in the physics of living systems in the direction of modeling of purposeful search behavior at the insect level. They enable systems of adaptive control, considering changes in environmental conditions and in properties of the controllable object in the control process, to be developed. The algorithm allows both traditional supervisory training of a neural network with a priori known answers and finish learning during its functioning in accordance with preassigned criteria when knowledge of required states of neuron network outputs is lacking. Krasnoyarsk State University; Institute of Biophysics of the Siberian Branch of the Russian Academy of Sciences. Translated from Izvestiya Vysshikh Uchebnykh Zavedenii, Fizika, No. 6, pp. 47–51, June, 2000.     

基于高频雷达多普勒谱预测风向的一种新方法

通过高频雷达一阶多普勒谱与海上风向的关系, 提出了一种基于神经网络和多波束采样法相结合预测海面风向的新方法. 在神经网络不同输入、输出参数情况下对扩展因子为奇数时的仿真数据进行了风向预测, 并在扩展因子为偶数时结合多波束采样法进行风向预测, 消除了风向的模糊性. 通过预测数据和仿真数据对比, 发现两者符合较好. 从神经网络和多波束采样法相结合的预测结果中可以看出, 风向的误差大约为4°—6°, 风向扩展因子的平均误差为0.26, 为预测海面风场的研究提供一种新的思路和方法.     

Characterization of optically soft spheroidal particles by multiangle light-scattering data by use of the neural-networks method

A method for evaluating the size of optically soft spheroidal particles by use of the angular structure of scattered light is proposed. It is based on the use of multilevel neural networks with a linear activation function. The retrieval errors of radius R of the equivolume sphere and aspect ratio e are investigated. The ranges of the size of R, e, and the refractive index are 0.3-1.51 microns, 0.2-1, and 1.01-1.02, respectively. The retrieval errors of the equivolume radius and aspect ratio are 0.004 micron and 0.02, respectively, for a three-level neural network (at a precisely measured angular distribution of scattered light). The retrieval errors of R and e for a one-level neural network are 2-5 times greater. The errors for a multilevel neural network increase faster than those for a single-level network.     

Controlling Beam Halo-Chaos by Adaptive Control Exterior Magnetic Field

In this paper, the parametric adaptive method for controlling the beam halo-chaos in the periodic focusing channels of high-current proton linacs is presented. The study of proton beam halo-chaos based on controlled beam envelope equation and the results of particles-in-cell simulations for macro-particle beam show that the proton beam chaotic envelope as well as the beam rsm radius can be controlled to the beam matched radius using this method.For the Kapchinskij-Vladimirskij (K- V) distribution of initial proton beam, all statistical physical, quantities of the beam halo-chaos are largely reduced. This control method has an advantage of the control halo-chaos since the exterior magnetic field as controlled parameter can be rather easily adjusted in the periodical magnetic focusing channels for the experiment.     

Controlling Beam Halo-Chaos by Adaptive Control Exterior Magnetic Field

In this paper, the parametric adaptive method for controlling the beam halo-chaos in the periodic focusing channels of high-current proton linacs is presented. The study of proton beam halo-chaos based on controlled beam envelope equation and the results of particles-in-ceU simulations for macro-particle beam show that the proton beam chaotic envelope as well as the beam rsm radius can be controlled to the beam matched radius using this method. For the Kapchinskij-Vladimirskij (K-V) distribution of initial proton beam, all statistical physical, quantities of the beam halo-chaos are largely reduced. This control method has an advantage of the control halo-chaos since the exterior magnetic field as controlled parameter can be rather easily adjusted in the periodical magnetic focusing channels for the experiment.     

Application of the neural network method for determining the characteristics of homogeneous spherical particles

The problem of reconstructing the characteristics of disperse particles from measurements of scattered radiation is considered. To solve this problem, the neural network method, based on the approximation of the parameters of particles by a linear combination of the results of measurements, is used. The capabilities of the method are studied on the examples of the reconstruction of the radius and the refractive index of spherical particles from measurements (for example, in flow-type cytometers) of the luminance of radiation scattered by individual particles, as well as the reconstruction of the mean radius, the coefficient of variation, and the refractive index from measurements of the luminance of radiation scattered by an ensemble of particles. Errors in the reconstruction of the characteristics of disperse particles depending on the structure of the neural network and the parameters of particles are studied.     

Prediction of the acoustic form function by neural network techniques for immersed tubes

A new approach is used to predict the acoustic form function (FF) for an infinite length cylindrical shell excited perpendicularly to its axis using the artificial neural network (ANN) techniques. The Wigner-Ville distribution is used like a comparison tool between the FF calculated by the analytical method and that predicted by the ANN techniques for a stainless steel tube. During the development of the network, several configurations are evaluated for various radius ratios ba (a: outer radius: b: inner radius of the tube). The optimal model is a network with one hidden layer. It is able to predict the FF with a mean relative error about 1.61% for the cases studied in this paper.     

An optical method and neural network for surface roughness measurement

The measurement of surface roughness using stylus equipment has several disadvantages. A non-contact optical method is needed for measuring the surface roughness of engineering metals with improved accuracy. One candidate for an optical method is the use of a laser source, where the laser light intensity reflected from the surface represents the surface roughness of the illuminated area. A relation can be developed between the reflected laser beam intensity and the surface roughness of the metal. The present study examines the measurement of the surface roughness of the stainless steel samples using a He-Ne laser beam. In the measurement a Gaussian curve parameter of a Gaussian function approximating the peak of the reflected intensity is measured with a fast response photodetector. In order to achieve this, an experimental setup is designed and built. In the experimental apparatus, fiber-optic cables are used to collect the reflected beam from the surface. The output of the fiber-optic system is fed to a back-propagation neural network to classify the resulting surface profile and predict the surface roughness value. The results obtained from the present study are then compared with the stylus measurement results. It is found that the resolution of the surface texture improves considerably in the case of optical method and the neural network developed for this purpose can classify the surface texture according to the control charts developed mathematically.