Parallel chaos optimization algorithm with migration and merging operation

Chaos optimization algorithm (COA) utilizes the chaotic maps to generate the pseudo-random sequences mapped as the decision variables for global optimization applications. A kind of parallel chaos optimization algorithm (PCOA) has been proposed in our former studies to improve COA. The salient feature of PCOA lies in its pseudo-parallel mechanism. However, all individuals in the PCOA search independently without utilizing the fitness and diversity information of the population. In view of the limitation of PCOA, a novel PCOA with migration and merging operation (denoted as MMO-PCOA) is proposed in this paper. Specifically, parallel individuals are randomly selected to be conducted migration and merging operation with the so far parallel solutions. Both migration and merging operation exchange information within population and produce new candidate individuals, which are different from those generated by stochastic chaotic sequences. Consequently, a good balance between exploration and exploitation can be achieved in the MMO-PCOA. The impacts of different one-dimensional maps and parallel numbers on the MMO-PCOA are also discussed. Benchmark functions and parameter identification problems are used to test the performance of the MMO-PCOA. Simulation results, compared with other optimization algorithms, show the superiority of the proposed MMO-PCOA algorithm.     

A Neural Network Approach for Signal Detection in Digital Communications

In this paper, we propose a new approach for signal detection in wireless digital communications based on the neural network with transient chaos and time-varying gain (NNTCTG), and give a concrete model of the signal detector after appropriate transformations and mappings. It is well known that the problem of the maximum likelihood signal detection can be described as a complex optimization problem that has so many local optima that conventional Hopfield-type neural networks fail to solve. By refraining from the serious local optima problem of Hopfield-type neural networks, the NNTCTG makes use of the time-varying parameters of the recurrent neural network to control the evolving behavior of the network so that the network undergoes the transition from chaotic behavior to gradient convergence. It has richer and more flexible dynamics rather than conventional neural networks only with point attractors, so that it can be expected to have much ability to search for globally optimal or near-optimal solutions. After going through a transiently inverse-bifurcation process, the NNTCTG can approach the global optimum or the neighborhood of global optimum of our problem. Simulation experiments have been performed to show the effectiveness and validation of the proposed neural network based method for the signal detection in digital communications.     

舰船水声隐身技术(二)

本文探讨了水声隐身性能对舰船战技术性能的影响 ,舰船水噪声的主要噪声源、水噪声的治理措施及其展望。     

非理想信道下Colpitts混沌电路的自适应同步研究

本文研究了Colpitts混沌电路在非理想信道条件下的自适应同步问题。引入自适应控制器对输入到响应部分（response system）的衰变混沌信号进行预处理，来补偿衰落信道对于同步性能的影响。给出了系统的数学模型，对恒定信道衰变和时变信道衰变两种非理想信道条件下的混沌ColpiRs电路的自适应同步进行了数值仿真研究。仿真结果表明，对于恒定或慢变化的时变信道衰变，自适应控制器可以有效的提高Colpitts电路响应部分和驱动部分（Drive system）的同步性能。     

一种基于混沌的随机数发生器设计及其IC实现

在密码学、仿真学以及集成电路测试等许多领域 ,随机数起着重要的作用。在密码学中 ,通常要求所使用的随机数具有不可预测性。基于混沌现象 ,使用开关电容技术 ,用集成电路实现了一种硬件随机数发生器。测试结果表明 ,其产生的序列具有不可预测性 ,可以满足密码学的应用要求。     

混沌光学系统的前向神经网络系统辨识研究

为解决混沌光学系统自适应控制所需之控制参考动力学模型问题，以布拉格声光混沌系统的系统辨识为例．研究了利用前向神经网络对混沌光学系统进行系统辩识的可行性。计算机仿真实验发现．在静态ＢＰ算法支持下一结构十分简单的三层ＢＰ前向神经网络（１：４：１）即可在一定的精度范围内完成对布拉格声光混沌系统的系统辨识．此结果表明．三层前向ＢＰ神经网络在静态ＢＰ算法的支持下确是一良好的混沌光学系统辨识器，因而可用来处理混沌光学时间序列以进行混沌光学系统的动力学重构。     

瞬时吸收率检测与高频噪声的剔除

在高分辨率处理过程中，由于地震数据记录中存在着一类并不被人们十分注意的，但影响却十分严重的高频噪声，使得高分辨率处理结果陷入一种“混沌”状态。为此，本文仔细分析和研究了这类噪声产生的物理机制和原因，并提出瞬时吸收率检测与高频噪声剔除的方法。     

Transient aspects of inner wall temperatures at different spatial positions in fluidized bed evaporators

In this work, the instantaneous data of inner wall temperatures obtained at given axial positions but for different heated tubes or at various axial positions but at the same heated tube in a tube-bank fluidized bed evaporator with a vapor–liquid–solid external natural circulation flow were analyzed by employing linear and nonlinear time series analysis methods, including correlation dimension and Kolmogorov entropy analyses besides time domain, power spectrum and autocorrelation analyses. It is found that nonlinear characteristics of the wall temperature time series vary with spatial position in the evaporator at given operation conditions, and thus the multi-phase flow boiling behavior. However, the signals obtained at given axial position but different heated tubes in the evaporator exhibit more similar nonlinear evolution behavior, and those measured in the same heated tube but at different axial heights in the evaporator undergo more distinct nonlinear dynamic behavior. Multi-scale phenomena in different spatial positions are also found and discussed. These transient aspects of inner wall temperatures are closely related to the average holdup of solid particles and motion of vapor bubbles in vapor–liquid–solid flow. These results can provide some valuable information on the system modeling and controlling from the point of view of nonlinearity.     

256比特的混沌分组密码

由于密码分析研究的进展及DES自身的弱点,原64比特的DES将不能作为数据加密的标准算法而长期存在。在原来工作的基础上,本文提出了256比特的分组密码方案。密码算法由基于混沌映射的数字滤波器构造。