Impulsive quasi-synchronization of a class of coupling chaotic systems

The conditions to achieve quasi-synchronization of coupling chaotic systems by virtue of impulsive control are investigated in the cases of parameter mismatch. By means of detailed theoretical analysis and precise theoretical derivation,the error bound of Euclidean norm and the function relation of error bound,impulse interval and control gain are obtained. At last,the feasibility and effectiveness of this method is verified by MATLAB numerical simulation.     

一类混沌系统的函数矩阵投影同步

研究了一类混沌系统的函数矩阵投影同步问题，基于函数矩阵方法，利用Lyapunov稳定性理论和极点配置理论，设计了两个连续混沌系统之间的同步方案，同时设计了两个离散混沌系统之间的同步方案，实现了驱动系统与动态系统按给定的函数矩阵投影同步，并给出了证明，通过对Lorenz混沌系统，和Henon系统的数值模拟，表明了该方法的有效性.     

基于T-S模糊模型的线性系统混沌反控制

研究了连续稳定线性系统的混沌反控制问题。基于T-S模糊模型表示的非线性混沌系统,实现了连续稳定线性系统的混沌化。此方法不需要计算系统的Liyapunov指数,大大降低了计算量。仿真结果表明了该方法的有效性。     

Combined Method of Chaotic Theory and Neural Networks for Water Quality Prediction

Chaos theory was introduced for water quality, prediction, and the model of water quality prediction was established by combining phase space reconstruction theory and BP neural network forecasting method. Through the phase space reconstruction, the one-dimensional water quality time series were mapped to be multi-dimensional sequence, which enriched the spatial information of water quality change and expanded mapping region of training samples of BP neural network. Established model of combining chaos theory and BP neural network were applied to forecast turbidity time series of a certain reservoir. Contrast to BP neural network method, the relative error and the mean squared error of the combined method had all varying degrees of lower. Results indicated the neural network model with chaos theory had the higher prediction accuracy, at the same time, it had better fault-tolerant capability and generalization performance .     

基于阈值判决的无线传感器网络混沌信号重构

考虑在传感器网络中重构混沌信号的问题.各传感器同时观测一个混沌信号.由于带宽受限,各传感器量化观测信号并将量化后的数据发送到融合中心,该中心使用无先导卡尔曼滤波算法重构观测的混沌信号.为了节省能量,只有观测信号信噪比高于某个阈值的传感器被选中并发送数据.定义代价函数关联重构性能和能量消耗,分析并仿真了代价函数和阈值之间的关系.结果表明在某些情况下部分传感器发送数据更合理.     

基于混沌时间序列的马尾松角胫象林间诱捕数量短期预测

为了对马尾松角胫象的防治及松材线虫病的控制提供技术支持,利用混沌时间序列的基本原理,分析马尾松角胫象林间诱捕序列,并重构其相空间及计算最大Lyapunov指数。结果表明：马尾松角胫象林间诱捕序列具有混沌特性。在此基础上,应用加权一阶局域预测模型对马尾松角胫象种群动态进行短期预测,结果显示在最长可预报时间尺度内该模型的预测成功率为100%。     

Research Advance on Rockburst Theory and Its Engineering Application in Collieries

This paper summarizes and describes the progress in coal rockmass outburst theory in resent decades both at home and abroad,discusses the tendency of mechanism of rockburst,and gives a fullier review about the presently adopted prediction methods and preventive measures,simultaneously looks into the prospective development on the study of prediction and preventive measures and its practical applications.     

时变海流干扰下深远海渔业无人船多目标路径规划

深远海无人船在开发渔业资源时,面临着续航能力不足和路径规划算法收敛慢、精度低等问题,为尽可能减少渔业无人船在实际任务执行过程中环境影响和最大限度地优化航行路线,在保证其安全航行的前提下,设计了以路径长度、转舵和海流能耗等多个参数最小为目标的路径规划算法。通过对无人船在航行时海域环境和任务目标的分析,建立了时变海流干扰下的无人船多目标计算模型,采用改进的自适应灰狼优化算法进行求解,算法通过引入多项策略进行统筹优化。该算法应用于复杂水域下渔业无人船多目标优化领域的仿真实验,证实了算法的可行性和改进策略的有效性,多目标相较于3个单目标仿真结果对总目标值的优化率分别提高了9.2%、1.7%、11.9%;不同海流状态下的仿真路径表明了相较于传统的以距离最优算法能够节省更多的成本,有效地提高了无人船全局航迹的规划性能。     

Chaotic Time Series Analysis Based on Radial Basis Function Networks

A method based on radial basis function networks for forecasting chaotic time series is proposed.The nonlinear time series identification problem is formulated with a nonlinear autoregressive moving average(NARMAX)model then a new identification algorithm based on dynamic radial basis function networks is proposed.Then this method is applied to the estimation of embedding dimension for chaotic time series of Henon mapping and the confirmation of the chaotic phenomena in stock markets of China,from which one can get the desired results.Further research directions are also pointed out.