WRBF网络的设计及其在混沌时间序列中的应用

结合小波网络良好的时频特性和RBF网络良好的局部特性，设计小波径向基函数网络(WRBF)，该四层网络较三层小波网络和三层RBF网络有更优的特性。网络的第一隐层对输入样本进行小波映射，完成对输入信息的压缩，第二隐层实现径向基函数的非线性计算，克服了RBF网络在处理多维样本时神经元的中心点和宽度难以确定及网络结果往往较复杂的弱点。为实现对网络结构和参数的同时优化，提出用二进制一复数混合编码的自适应进化规划，利用双倍体基因拓展染色体的信息量，加快算法的收敛速度，实现全局优化。在算法研究的基础上，利用WRBF网络对混沌时间序列进行预测，验证了方法的有效性。     

粒子动力学演化算法及其在求解SOP上的应用

根据粒子输运理论中粒子相空间能量最小原理和熵增法则提出一类粒子动力学演化算法，并在数据实验中用该算法求解了一些传统演化算法难于求解的单目标优化问题(SOP)，比如求解凸函数和多峰函数等最小优化问题。由于这种新的动力学演化算法能使几乎所有的个体参加杂交和变异，因此，与传统的演化算法相比，该方法不仅能较快速地求解线性和非线性优化问题，而且还能容易地求出达到最优解的多个坐标点。     

制导航空弹药模糊变结构制导律的设计

为了使航空弹药具有攻击大型地面静止目标的能力,将其组合导航系统实时传输的位置和速度信息进行坐标转换,根据转换的信息建立弹体与目标的相对运动模型.应用变结构控制理论,引入模糊规则自适应地调整趋近律,设计了制导律.仿真表明在各种干扰条件下,提高了制导精度.     

一种求解多目标优化问题的混合演化算法

提出了一种混合演化算法求解多目标优化问题.演化算法是解决多目标优化问题的有效方法,在全局优化问题中具有很好的鲁棒性,但其局部搜索性能有待改善.Hooke and Jeeves方法是一经典的局部搜索算法,将其与演化算法结合求解多目标优化问题,提高了解的收敛质量,因而从整体上提高了算法的性能,并且测试结果也说明了该算法的可行性.     

四合木(TetraenamongolicaMaxim)有性繁殖能力的观测

通过对２０条１００ｍ样线上４７５株四合木植株的观测，明显可见的开花结实植株仅占１１％左右；又对２３２个单株的开花结实作了统计，平均１９８朵花／株，应有７９０粒果／株，实际结实１８０粒／株，结实率２２．８％．作为一个以有性繁殖方式延续后代的种群，以上结果表现出四合木种群有性繁殖能力的衰退，这可以作为四合木种群进化潜能衰退证据的一部分     

把握导向贴近师生──校报提高宣传水平之管见

本文从理论和实践的结合上，阐述了高校校报必须从二个方面下功夫，才能把握好正确导向，提高引导水平，发挥校报在学校社会主义精神文明建设中的积极作用。     

Some massively parallel algorithms from nature

We introduced the work on parallel problem solvers from physics and biology being developed by the research team at the State Key Laboratory of Software Engineering, Wuhan University. Results on parallel solvers include the following areas: Evolutionary algorithms based on imitating the evolution processes of nature for parallel problem solving, especially for parallel optimization and model-building; Asynchronous parallel algorithms based on domain decomposition which are inspired by physical analogies such as elastic relaxation process and annealing process, for scientific computations, especially for solving nonlinear mathematical physics problems. All these algorithms have the following common characteristics: inherent parallelism, self-adaptation and self-organization, because the basic ideas of these solvers are from imitating the natural evolutionary processes. Foundation item: Supported by the National Natural Science Foundation of China (No. 60133010, No. 70071042, No. 60073043) and National Laboratory for Parallel and Distributed Processing Biography: Li Yan (1974-), female, Ph. D candidate, research direction: evolutionary computation.     

Advances in simulated modeling of vibration systems based on computational intelligence

Computational intelligence is the computational simulation of the bio-intelligence, which includes artificial neural networks, fuzzy systems and evolutionary computations. This article summarizes the state of the art in the field of simulated modeling of vibration systems using methods of computational intelligence, based on some relevant subjects and the authors' own research work. First, contributions to the applications of computational intelligence to the identification of nonlinear characteristics of packaging are reviewed. Subsequently, applications of the newly developed training algorithms for feedforward neural networks to the identification of restoring forces in multi-degree-of-freedom nonlinear systems are discussed. Finally, the neural-network-based method of model reduction for the dynamic simulation of microelectromechanical systems (MEMS) using generalized Hebbian algorithm (GHA) and robust GHA is outlined. The prospects of the simulated modeling of vibration systems using techniques of computational intelligence are also indicated.     

Mixed search algorithm for protein folding

0　IntroductionThedeterminationofthefoldingpathwayfollowedbyaproteinintheconformationalspacefromthedenaturedstatetothenativestructureisstillanunsolvedproblem .Itsdifficultyismainlyduetotheenormousextensionoftheconformationalspaceandtoitsexponentialgrowthwhenthelengthoftheproteinchainincreases.HowaproteinsolvestheLevinthalparadox[1 ] ,i.e.,howitfoldstoitsnativestructurewithoutanexhaustivesearchintheconforma tionalspace,hasbeenlargelydebatedinRef.[2 5 ].Thenatureofthedrivingforcesinducingtheco…