基于变尺度寻优和遗传搜索技术的模糊神经网络全局学习算法

本文给出了一种改进的拟牛顿算法与具有新型交配方式和可变变异概率的遗传算法相结合的全局寻优算法，用以搜索模糊神经网络误差函数的全局量小点。     

Discrete frequency-coded radar signal design

A modified simulated annealing algorithm (MSAA) is proposed as combinatorial multivariable optimisation technique to design discrete frequency-coding (DFC) sequence sets with good auto- and cross-correlation properties. The proposed algorithm is a combination of simulated annealing and Hamming scan algorithm. MSAA has global minimum estimation capability of simulated annealing and fast convergence rate of Hamming scan algorithm. Some of the synthesised results are presented, the properties of the sequence sets are shown to be better than the other sequence sets known in the literature. Synthesised DFC sequence sets have properties far better than polyphase sequence sets. The synthesised DFC sequence sets are promising for practical application to netted radar/multiple radar systems.     

超磁致伸缩执行器磁滞模型的参数辨识

准确辨识磁滞模型参数是保证超磁致伸缩执行器位移控制精度的关键,而单一算法难以实现对超磁致非线性模型参数的精确辨识。该文提出了一种新型混合优化策略,即改进的遗传退火算法,并将其应用于对超磁致伸缩执行器位移磁滞模型参数的辨识。该算法兼顾了遗传算法和模拟退火算法的优点,同时还引入了机器学习原理,将模拟退火算法作为遗传算法中的种群变异算子,并将模拟退火算法中的抽样过程与遗传算法相结合。此算法不仅充分发挥了遗传算法并行搜索能力强的特点,且增强和改进了遗传算法的进化能力,同时提高了系统的收敛性和收敛速度,避免最优解的丢失。通过仿真和试验研究表明,该算法相对于遗传算法有更高的精度,可有效精确辨识超磁致伸缩执行器磁滞模型的参数。     

基于遗传退火算法的MTI 滤波器设计方法

动目标显示(MTI)是雷达杂波抑制的重要技术之一,MTI 滤波器系数和脉冲参差比设计对MTI 的抗杂波性能至关重要。遗传退火算法将模拟退火算法过程溶入遗传算法,同时克服了遗传算法容易陷入局部最优和模拟退火算法收敛速度慢的缺点。文中介绍了一种将遗传退火算法应用于MTI 滤波器的设计方法,该方法可找到最优MTI 滤波器。实验结果表明,针对给定的MTI 滤波器设计要求,该方法能快速完成优化遍历,优化后滤波器性能提升明显。     

基于变异算子与模拟退火混合的人工鱼群优化算法

人工鱼群算法(AFSA)是一种新型的群智能随机全局优化技术.本文在分析AFSA存在不足的基础上,提出了基于变异算子与模拟退火混合的人工鱼群优化算法.该算法保持了AFSA算法简单、易实现的特点,克服了人工鱼漫无目的随机游动或在非全局极值点的大量聚集,显著提高了算法的运行效率和求解质量.通过函数和实例测试验证,表明了该算法是可行和有效的.     

一种进化模拟退火矢量量化图像编码新算法

模拟进化和模拟退火是解决全局优化问题的随机搜索技术,它们在工程领域有着广泛的应用.本文将这两种技术有机结合,提出了一种进化模拟退火算法,并用其解决图像编码矢量量化码书设计问题.实验表明,算法具有收敛速度快、码书性能佳等优点.     

Constrained nonlinear optimization approaches to color-signalseparation

Separating a color signal into illumination and surface reflectance components is a fundamental issue in color reproduction and constancy. This can be carried out by minimizing the error in the least squares (LS) fit of the product of the illumination and the surface spectral reflectance to the actual color signal. When taking in account the physical realizability constraints on the surface reflectance and illumination, the feasible solutions to the nonlinear LS problem should satisfy a number of linear inequalities. Four distinct novel optimization algorithms are presented to employ these constraints to minimize the nonlinear LS fitting error. The first approach, which is based on Ritter's superlinear convergent method (Luengerger, 1980), provides a computationally superior algorithm to find the minimum solution to the nonlinear LS error problem subject to linear inequality constraints. Unfortunately, this gradient-like algorithm may sometimes be trapped at a local minimum or become unstable when the parameters involved in the algorithm are not tuned properly. The remaining three methods are based on the stable and promising global minimizer called simulated annealing. The annealing algorithm can always find the global minimum solution with probability one, but its convergence is slow. To tackle this, a cost-effective variable-separable formulation based on the concept of Golub and Pereyra (1973) is adopted to reduce the nonlinear LS problem to be a small-scale nonlinear LS problem. The computational efficiency can be further improved when the original Boltzman generating distribution of the classical annealing is replaced by the Cauchy distribution.     

基于泛函连接网络和差分进化算法的后非线性混叠信号盲分离方法

把后非线性混叠信号盲分离的分离系统用泛函连接网络来建模,对分离系统的输出应用高阶统计量独立性准则作为测度,然后利用差分进化算法对泛函连接网络的权值进行学习,从而获得了一种后非线性混叠信号盲分离算法。由于泛函连接网络是一种单层神经网络,具有学习参数少、收敛速度快和非线性逼近能力强的特点;而差分进化算法控制参数少、易于选择、具有全局寻优能力和快速的收敛特性;因而与其它的后非线性混叠信号盲分离方法相比,该文提出的分离算法具有计算简单、收敛速度快、较高的精度和稳定性好的特点。仿真结果显示了这种方法是可行和有效的。     

Adaptive neuro-fuzzy control of a flexible manipulator

This paper describes an adaptive neuro-fuzzy control system for controlling a flexible manipulator with variable payload. The controller proposed in this paper is comprised of a fuzzy logic controller (FLC) in the feedback configuration and two dynamic recurrent neural networks in the forward path. A dynamic recurrent identification network (RIN) is used to identify the output of the manipulator system, and a dynamic recurrent learning network (RLN) is employed to learn the weighting factor of the fuzzy logic. It is envisaged that the integration of fuzzy logic and neural network based-controller will encompass the merits of both technologies, and thus provide a robust controller for the flexible manipulator system. The fuzzy logic controller, based on fuzzy set theory, provides a means for converting a linguistic control strategy into control action and offering a high level of computation. On the other hand, the ability of a dynamic recurrent network structure to model an arbitrary dynamic nonlinear system is incorporated to approximate the unknown nonlinear input–output relationship using a dynamic back propagation learning algorithm. Simulations for determining the number of modes to describe the dynamics of the system and investigating the robustness of the control system are carried out. Results demonstrate the good performance of the proposed control system.     

Fuzzy Feedback Linearization Control for MIMO Nonlinear System and Its Application to Full-Vehicle Suspension System

The paper presents a novel fuzzy feedback linearization control of nonlinear multi-input multi-output (MIMO) systems for the tracking and almost disturbance decoupling (ADD) performances based on the fuzzy logic control (FLC). The main contribution of this study is to construct a controller, under appropriate conditions, such that the resulting closed-loop system is valid for any initial condition and bounded tracking signal with the following characteristics: input-to-state stability with respect to disturbance inputs and almost disturbance decoupling. The feedback linearization control guarantees the almost disturbance decoupling performance and the uniform ultimate bounded stability of the tracking error system. As soon as the tracking errors are driven to touch the global final attractor with the desired radius, the fuzzy logic control immediately is applied via a human expert’s knowledge to improve the convergence rate. One example, which cannot be solved by the previous paper on the almost disturbance decoupling problem, is proposed in this paper to exploit the fact that the tracking and the almost disturbance decoupling performances are easily achieved by the proposed approach. In order to demonstrate the applicability, this paper has investigated a full-vehicle suspension system.     

基于等变化自适应源分离算法的滚动轴承故障信号自适应盲提取

针对复杂工况下滚动轴承故障信号盲提取问题,该文提出一种独立分量分析(ICA)中非线性函数自适应选择方法,解决了等变化自适应源分离算法(EASI)在多类振动源共存的情况下无法分离轴承故障信号的问题。此外,为了解决在线盲分离算法稳态误差与收敛速率的平衡问题,提出基于模糊逻辑的自适应迭代步长选择方法,极大地提高了学习算法的收敛速度,且稳态误差更小。轴承故障数据的盲提取仿真结果验证了算法的性能。     

面向高铁无线网络覆盖与容量优化的多agent模糊强化学习算法

为了提升高铁沿线LTE无线网络服务质量,提供最理想的覆盖与容量性能,在传统单agent学习算法的基础上,提出了通过多agent联合调整相邻eNodeB的天线下倾角从而实现覆盖与容量优化的模糊强化学习算法。并在LTE网络下的高速场景中进行仿真,仿真结果表明多agent学习算法与传统学习算法相比在高速环境下达到全局最优解的速率更快,特别是在应对环境突变的情况时恢复到最优解的速率有所提升。     

基于函数复杂度的自适应模拟退火和禁忌搜索新算法

在求解多峰复杂函数的过程中,传统的模拟退火算法和禁忌搜索算法经常出现算法快速收敛于局部最优解、后期收敛速度变慢和搜索能力变差等问题.为解决这些问题,本文给出函数复杂度的定义,并提出基于函数复杂度的自适应模拟退火和禁忌搜索算法.该算法首先根据函数复杂度自适应调整步长控制参数,然后根据调整后步长求得函数的粗糙解,在此基础上再使用初始步长求得全局最优解.实验表明,该算法不仅可以跳出局部最优解的限制,并且减少了迭代次数,有效地提高了全局和局部搜索能力.     

模糊非基因信息记忆的双克隆选择算法

该文针对传统智能优化算法中虚拟碰撞而导致的全局搜索效率降低的问题,提出一种模糊非基因信息记忆的双克隆选择算法。该算法设计基于模糊非基因信息的搜索机制与克隆选择原理相结合,对抗体进化中的非基因信息进行采集、模糊化并保存到记忆库,运用这些信息引导该抗体后续的双克隆搜索过程,从而减少非优区域的虚拟碰撞,提高全局搜索效率。通过标准测试函数的仿真试验并与其他算法比较,新算法表现出更快的全局收敛速度和更高的全局收敛精度。     

Hybrid social cognitive optimization algorithm for constrained nonlinear programming

To improve the global convergence speed of social cognitive optimization (SCO) algorithm,a hybrid social cognitive optimization (HSCO) algorithm based on elitist strategy and chaotic optimization is pr...     

整体退火遗传算法在不等间距线天线阵的综合中的应用

将一类非比例选择(整体退火选择)、保证收敛且易于判断收敛的新型遗传算法应用于不等间距天线阵的综合,对天线阵的位置和加权系数进行了优化。实验实例表明:该算法收敛速度快,有极强的避免过早收敛及避免局部极值的全局优化的能力。该方法为大型天线阵的优化设计提供了工具。     

基于整体退火遗传算法的不等间距天线阵的综合

将一类非时齐（整体退火选择）、保证收敛且易于判断收敛的新型遗传算法应用于不等间距天线阵的综合，对天线阵的位置及加权系数进行了优化。实验表明：该算法收敛速度快，有极强的避免过早收敛及避免局部极值的全局优化能力。该方法为大型天线阵的优化设计提供了方便     

Recurrent-Fuzzy-Neural-Network-Controlled Linear Induction Motor Servo Drive Using Genetic Algorithms

A recurrent fuzzy neural network (RFNN) controller based on real-time genetic algorithms (GAs) is developed for a linear induction motor (LIM) servo drive in this paper. First, the dynamic model of an indirect field-oriented LIM servo drive is derived. Then, an online training RFNN with a backpropagation algorithm is introduced as the tracking controller. Moreover, to guarantee the global convergence of tracking error, a real-time GA is developed to search the optimal learning rates of the RFNN online. The GA-based RFNN control system is proposed to control the mover of the LIM for periodic motion. The theoretical analyses for the proposed GA-based RFNN controller are described in detail. Finally, simulated and experimental results show that the proposed controller provides high-performance dynamic characteristics and is robust with regard to plant parameter variations and external load disturbance     

移动机器人轨迹跟踪的模糊PID-P型迭代学习控制

本文针对移动机器人轨迹跟踪控制问题的研究,提出了一种基于移动机器人运动模型的模糊开闭环PID-P型非线性离散迭代学习控制方法,给出了PID-P型迭代学习的收敛条件及其证明过程,并采用模糊控制的原理整定PID三个学习增益矩阵的参数.该控制方法提高了移动机器人对特定轨迹的重复跟踪能力,具有算法实现简单的特点.实验仿真结果表明,采用模糊开闭环PID-P型迭代学习控制算法对轨迹跟踪是可行有效的.     

基于改进的引力搜索算法的T-S模型辨识

针对引力搜索算法在求解复杂问题时搜索精度较低、易出现早熟收敛的缺点。提出一个新颖的智能算法-基于基因突变的引力搜索算法来辨识T-S模型的参数,同时提出一种改进的聚类算法辨识T-S模型的结构,实验结果表明,改进算法辨识出的T-S模型结构紧凑、精度更高,且泛化能力强。