超混沌遗传算法在多约束QoS组播路由中的应用研究

针对多约束QoS组播路由的优化问题,提出了一种超混沌遗传混沌算法.该算法利用遗传算法中的改进的适应度函数,通过结合超混沌映射优越性的搜索能力,对遗传算法选出的个体进行混沌优化,以改善遗传算法过早陷入早熟的情况.通过仿真实验表明,该算法有效地改进了搜索效率,且收敛速度更快更稳定,是一种解决多约束QoS路由问题可行和有效的方法.     

改进的蚂蚁算法在矩形优化排料中的应用

矩形件优化排料问题是一类具有NP完全难度的组合优化问题.将改进的蚂蚁算法应用到矩形件优化排料问题求解中,优化试验结果表明可获得比基本蚂蚁算法更好的效果,为矩形件优化排料这类NP完全问题提供了新的思路和方法.     

地磁辅助惯性组合导航技术分析

从地磁导航的特点出发,指出了地磁／惯性组合导航在未来发展中的优势,并对实现该种组合导航需要的技术手段做了全面的分析,包括：信息融合策略、高精度地磁场模型的构建方式以及滤波算法的设计。同时,阐述了其中的改进方向,为进一步提高该种组合导航性能提供思路。     

基于粒子群算法的PID神经网络解耦控制

基于粒子群的优化算法具有对整个参数空间进行高效并行搜索的特点以及PID神经网络的自调节和自适应特性,设计了具有PID结构的多变量自适应神经网络控制器。该算法采用粒子群算法优化PID神经网络初始权值,并将优化后的最优初始权值控制非线性耦合系统。系统仿真结果表明,粒子群优化后的PID神经网络控制器具有逼近控制目标更快、响应时间较短的显著优点。该控制策略可在大范围内克服系统的非线性和强耦合问题,具有一定的理论研究价值和工程实用价值。     

Adaptive Control of the Electro‐Hydraulic Servo‐System with External Disturbances

The paper deals with the structure of the adaptive control electro‐hydraulic servo‐system (EHSS) with external load disturbances, practical verification of the identification, and control algorithms. The electro‐hydraulic servo system composed of a servo‐cylinder controlled with a servo‐valve is discussed. It is a strongly nonlinear object with parameters changing over time. Adaptive adjuster parameters were determined by means of current identification resulting in the parametric model. Identification was conducted on the basis of measurement of the controlling size and regulated size objects. The identified model of the object was applied to carry out the on‐line synthesis of the proportional–integral–derivative (PID) controller. The selected problems connected with obtaining the algorithm of adaptive control are presented. The computer program for implementing the algorithm with numerical simulation and identification of the control physical model object were calculated. The aim of the research was to examine the effectiveness of the adaptive control method in an electro‐hydraulic servo system, both theoretically and experimentally.     

A novel artificial bee colony algorithm with Powell's method

Artificial bee colony (ABC) algorithm is a relatively new optimization technique which has been shown to be competitive to other population-based algorithms. However, there is still an insufficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. To address this concerning issue, we first propose a modified search equation which is applied to generate a candidate solution in the onlookers phase to improve the search ability of ABC. Further, we use the Powell's method as a local search tool to enhance the exploitation of the algorithm. The new algorithm is tested on 22 unconstrained benchmark functions and 13 constrained benchmark functions, and are compared with some other ABCs and several state-of-the-art algorithms. The comparisons show that the proposed algorithm offers the highest solution quality, fastest global convergence, and strongest robustness among all the contenders on almost all test functions.     

基于遗传优化的BP神经网络法在甲烷检测中的应用

针对传统的最小二乘法拟合红外传感器的输出特性曲线时存在误差大、计算复杂,传统的BP神经网络法拟合红外传感器的输出特性曲线时存在网络收敛速度慢、易陷入局部极小的问题,通过分析改进的最小二乘法和改进的基于遗传优化的BP神经网络法的拟合效果,指出改进的BP神经网络法拟合度较高,并给出了改进的BP神经网络法在甲烷体积分数检测中的实验结果。结果表明,该方法能够拟合出理想的曲线,有效提高了红外传感器的检测精度及响应速度。     

基于混合变异策略的改进差分进化算法及函数优化

针对差分进化算法DE 传统变异策略不能有效平衡全局搜索和局部搜索,并且算 子固定,导致算法早收敛、搜索效率较低。基于DE 变异策略性能,提出一种混合变异策略, 力图平衡算法探索和开发能力,使得前期增强全局搜索,保持种群多样性; 后期偏重局部搜 索,尽快收敛到全局最优值。同时操作算子采用随机正态缩放因子F 和时变交叉概率因子CＲ, 进一步改善算法性能。几个典型Benchmarks 测试函数实验表明: 该改进型差分进化算法能有 效避免早收敛,较好地提高算法的全局收敛能力和搜索效率。     

基于遗传算法的输出反馈动态面优化控制

针对一类只有输出信号可测且非线性项含不可测状态的不确定系统,基于输入信号置换思想构建了神经网络状态观测器,结合动态面理论设计了输出反馈自适应控制方案。通过理论分析确定了控制律参数选取范围,保证了闭环控制系统所有信号半全局一致终结有界; 利用遗传算法提出一种参数寻优策略,选取了最优的输入初始值和控制律参数,解决了控制冲击问题,提高了跟踪精度。