Self-Organizing Transient Chaotic Neural Network for Cellular Channel Assignment

This paper presents a self-organizing transient chaotic neural network to solve the channel assignment problem, one of NP-complete problems. The proposed neural network consists of two parts. The first part is the self-organizing evolution stage, which based on the mutual inhibition mechanisms of bristle differentiation and the problem's heuristic information. The second part is the transient chaotic neural network executing stage. A significant property of the TCNN model is that the chaotic neurodynamics is temporarily generated for searching and self-organizing in order to escape the local minima. In the proposed neural network, the first part is used to improve the quality of the obtained solutions. The simulating results have shown that the self-organizing transient chaotic neural network improves greatly performance through solving the well-known benchmark problems, especially for the Sivarajan's and Kunz's benchmark problems, while the performance is comparable with existing algorithms.     

非线性控制在电力系统中的应用

对多种非线性控制技术的基本原理进行了简要的介绍, 在此基础上结合电力系统的具体特性介绍了它们在电力系统中的一些具体应用. 最后, 指出了电力系统非线性控制的发展趋势.     

混沌鲶鱼粒子群优化和差分进化混合算法

为了改善粒子群优化算法的性能,引入了"鲶鱼效应"思想,改造粒子群个体的进化策略,用混沌方法改良了种群搜索策略,把这两者结合起来,既提高种群的广度搜索能力,又提升深度搜索能力,跟差分进化算法进行混合,算法优势互补,形成一种新型的混合算法,更好地协调广度搜索和深度搜索之间的矛盾,提升算法性能。经过对三个标准函数的测试,仿真结果表明该算法在逃离局部陷阱能力和搜索精度均有显著提高。     

时空混沌对可激发介质中螺旋波的影响的研究

心肌细胞里离子通道的随机打开,在心肌组织中产生的空间分布电流就是时空混沌的.为了了解时空混沌对螺旋波的影响,基于B(a|¨)r模型研究了两层耦合可激发介质系统中螺旋波的演化,提出无条件和有条件两种驱动响应耦合.耦合前,驱动和响应子系统分别处于时空混沌态和螺旋波态.在不同的参数下,发现螺旋波表现出不同的动力学行为.在很小的耦合强度下,时空混沌对螺旋波动力学行为几乎无影响.当耦合强度很大时,无条件耦合总是导致螺旋波的破碎.当相关参数适当选取时,时空混沌既能提高受其作用介质的激发性,也能降低它的激发性.此外,它还能使稳定螺旋波和漫游螺旋波作无规漫游或无规漂移,甚至导致螺旋波漂移出系统;对于不稳定螺旋波,时空混沌能极大延迟螺旋波出现破碎.特别是,在有条件耦合下,可以使不稳定螺旋波成为稳定或漫游螺旋波.     

Stagnation, chaos, and severe character neuroses.

There are a relatively large number of unexplored issues in treating severely neurotic, character-disordered patients. The defining characteristics of these patients are formulated in dynamic, structural, and object relational terms. A discussion of the masochistic character is presented to exemplify such features. It is suggested that treatment is an inescapable struggle due to the distinctly created transference-countertransference ambience wherein character resistances emerge as therapeutic stalemates. The milieus characterized by stagnation and chaos are examined in relation to clinical examples. Finally, three essential technical issues are considered in terms of therapists' capacity to "hold" patients' externalized material, use the countertransference, and sooner or later, interpret primitive defensive constellations and transferences. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

基于加密技术的DCT域图像公开水印算法

DCT变换是当前JPEG压缩中所使用的一个处理过程,它能把图像的能量集中到左上角的低频系数上,该文利用这个特点有效地实现了水印的鲁棒性与透明性的平衡。此外,为了进一步提高水印的鲁棒性与安全性,该算法有机地结合了重采样置乱和混沌加密等方法对水印信息进行加密预处理。实验表明,该算法高效、安全性能好,且具有较好的不可感知性,对常见的图像处理及剪切攻击等具有较好的鲁棒性。     

Neural and Super-Turing Computing

``Neural computing' is a research field based on perceiving the human brain as an information system. This system reads its input continuously via the different senses, encodes data into various biophysical variables such as membrane potentials or neural firing rates, stores information using different kinds of memories (e.g., short-term memory, long-term memory, associative memory), performs some operations called ``computation', and outputs onto various channels, including motor control commands, decisions, thoughts, and feelings. We show a natural model of neural computing that gives rise to hyper-computation. Rigorous mathematical analysis is applied, explicating our model's exact computational power and how it changes with the change of parameters. Our analog neural network allows for supra-Turing power while keeping track of computational constraints, and thus embeds a possible answer to the superiority of the biological intelligence within the framework of classical computer science. We further propose it as standard in the field of analog computation, functioning in a role similar to that of the universal Turing machine in digital computation. In particular an analog of the Church-Turing thesis of digital computation is stated where the neural network takes place of the Turing machine.     

VISUALIZATION OF JULIA ATTRACTIVE CYCLE AND GAUSS SUMS

The attractive cycle and its visualization,Julia set and its image,the fractal sequenceand quadratic Gauss sums are stated briefly.The variation of Gauss Sums results in generatingnew complex chaos image on computer monitor by aid of computer graphics.A set of illustrationsdisplayed on SGI 4D/25 workstation and by using C language in CAD laboratory,Institute ofComputing Technology,Academia Sinica.     

A new chaos detector

This paper proposes a new detector for chaos. It is simpler and numerically less intensive than previous methods. It is more robust than previous methods. It works well even with short data sets (200 time scalar points), in the presence of noise (as low as 4 dB signal to noise ratio in stationary additive white Gaussian noise), and with severe data level quantization (data quantized to five bits). The theory behind the new detector is given, and examples of its application to the Logistic, Henon, and Lozi Maps are shown. The new chaos detector is briefly compared to existing methods such as the estimation of the largest Lyapunov exponent, the correlation or attractor dimension, and the entropy of the data set. Conventional electrical engineering signal analysis tools (such as the Discrete Fourier Transform) fail to help in the detection of chaos. This failure is explored. Sinusoids and several types of noise are input to the detector and the outputs are recorded. The basis for a binary signaling scheme which uses chaotic sequences and the new chaos detector is briefly described.     

Introduction to the special section on chaos theory and psychological assessment.

The premise of the articles in this special section is that the goals of psychological assessment, and the explanations and predictions made on the basis of it, can be advanced by assimilating several concepts from nonlinear dynamical modeling and chaos theory—concepts and mathematical models that have been discussed most frequently in economics, ecology, biology, and physics. The concepts presented in the two articles by Heiby (1995a, 1995b, in this issue) and the article by Haynes, Blaine, and Meyer (1995, in this issue) are brief introductions to a small set of ideas from much larger domains (e.g., Burton, 1994). More extensive discussions of nonlinear dynamics and chaos can be found in several recently published books (Baker & Gollub, 1990; ?ambel, 1993; Haynes, 1992; Vallacher & Nowak, 1994; von Eye, 1990; Wei, 1990). These three articles borrow ideas that have been useful in other disciplines to address several phenomena that have been problematic in psychological assessment: (a) Behavior and causal variables often change rapidly in magnitude, rate, and form over time—they are dynamic; (b) these changes are often unpredictable, nonlinear, and discontinuous; and (c) it is often difficult to establish causal relationships for behavior change. The articles suggest several measurement strategies to increase our abilities to predict and explain the dynamical, nonstationary, and nonlinear phenomena that are often the targets of psychological assessment. (PsycINFO Database Record (c) 2010 APA, all rights reserved)