基于P系统的粒子群优化算法

为了克服算法早熟收敛问题并提高算法精度, 引入了膜计算理论。将PSO算法与P系统相结合, 提出了一种基于P系统的粒子群优化算法 (P-PSO), 有效地平衡粒子群的全局搜索和局部寻优。采用常用的三个测试函数对新算法进行了实验, 结果表明, 提出的P-PSO算法有效地解决了算法早熟问题, 提高了算法的收敛精度。由此可见, P-PSO算法能够有效改进原有PSO算法的性能。     

自适应改进和声—单纯形进化算法研究

针对和声搜索算法的不足,提出了一种自适应改进和声—单纯形进化算法(AIHSEA)。通过在新算法中加入变异策略对和声微调进行改进来增强算法的鲁棒性;适时执行单纯形算子增加群体搜索的方向性来加快搜索;采用自适应参数HMCR、PAR和BW调节全局和局部搜索。采用六个标准的优化算法测试函数对AIHSEA进行测试,并与HS、IHS和GHS算法进行对比,仿真结果表明AIHSEA算法具有较强的精确寻优和跳出局部最优的能力。     

Preferential adsorption coefficient of polymers

The recent formulation of the preferential adsorption coefficient, λ, which takes into account differences in molecular contact surface and in free volume, is tested by comparing theoretical with experimental values of λ from the literature. Seven different systems containing polystyrene and poly(methyl methacrylate) are considered. Agreement between theory and experiment is reached by treating the contact surface of the polymer as a fitting parameters, s. The adjusted values of s are: (a) systematically higher than the ones calculated from chain geometry; (b) largest in systems containing specific interactions (methanol). The connection between this enhanced apparent contact surface of the polymer and the ternary interaction parameter of the classical theory of λ, is analysed.     

MCU平台的嵌入式系统软件设计研究

嵌入式系统的运行需要可靠性和实时性的保证,因此良好的可靠性和实时性是嵌入式系统软件设计的重要标准。同时为了使软件系统的鲁棒性更强,嵌入式系统的设计需要一个非常好的软件架构。这里介绍了一种基于MCU平台的嵌入式系统软件架构方法。在这个方法中,事件驱动机制的引入满足了对软件架构的要求,主程序中不仅采用了优先调度机制,同时也加入了软件抗干扰措施,这些都使系统的实时性和可靠性得到了明显的提高。设计实验比较由该方法得到的软件架构与原始软件架构的实时性,得出由该方法优化后的软件架构确实具有更好的性能。     

无线网络工程优化方法研究

随着科技的进步,移动通信技术也得到迅速发展,人们对于通信质量的要求也越来越高。无线网络优化工程对于无线网络的建设起到非常重要的作用,它不仅能够解决无线网络中存在的问题,同时能够起到优化资源配置的作用。本文对网络优化工程技术的必要性以及优化内容进行分析,提出了网络优化中常见的问题以及解决措施,     

CDMA2000网络优化及典型掉话案例分析

网络覆盖状况是衡量移动通信网络性能优劣的关键,CDMA2000网络的覆盖、容量和质量相互制约。本论文介绍了CDMA2000网络优化流程,面临的挑战,路测中需重点观察的指标,常见的掉话原因,对典型的掉话案例越区覆盖进行分析,对从事网络优化工作者有一定的借鉴意义。     

LTE无线网络优化关键性能指标探讨

LTE 无线网络优化在整个无线通信网络的建设过程之中,这是十分重要的基础环节,它对无线通信网络的建设是不是能够科学地实施是很关键的。本文主要介绍了LTE系统的发展历史、TD-L的标准化历程,并对LTE无线网络优化的关键性能指标进行探讨。     

Quadratic approximate dynamic programming for input‐affine systems

We consider the use of quadratic approximate value functions for stochastic control problems with input‐affine dynamics and convex stage cost and constraints. Evaluating the approximate dynamic programming policy in such cases requires the solution of an explicit convex optimization problem, such as a quadratic program, which can be carried out efficiently. We describe a simple and general method for approximate value iteration that also relies on our ability to solve convex optimization problems, in this case, typically a semidefinite program. Although we have no theoretical guarantee on the performance attained using our method, we observe that very good performance can be obtained in practice.Copyright © 2012 John Wiley & Sons, Ltd.     

An approach for minimizing a quadratically constrained fractional quadratic problem with application to the communications over wireless channels

Studies for the cognitive model are relatively new in the literature; however there is a growing interest in the communication field nowadays. This paper considers the cognitive model in the communication field as the problem of minimizing a fractional quadratic problem, subject to two or more quadratic constraints in complex field. Although both denominator and numerator in the fractional problem are convex, this problem is not so simple since the quotient of convex functions is not convex in most cases. We first change the fractional problem into a non-fractional one. Second, we consider the semi-definite programming (SDP) method. For the problem with m (m≤2) constraints, we use the SDP relaxation and obtain the exact optimal solution. However, for the problem with m (m>2) constraints, we choose the randomization method to gain an approximation solution in the complex case. At last, we apply this method to practical communications over wireless channels with good results.