Cauchy Biogeography-Based Optimization based on lateral inhibition for image matching

In this paper, a hybrid method of Cauchy Biogeography-Based Optimization (CBBO) and Lateral Inhibition (LI) is proposed to complete the task of complicated image matching. Lateral inhibition mechanism is adopted for image pre-process to make the intensity gradient in the image contrastively strengthened. Biogeography-Based Optimization (BBO) is a bio-inspired algorithm for global optimization which is based on the science of biogeography, searching for the global optimum mainly through two steps: migration and mutation. To promote the optimization performance, an improved version of the BBO method using Cauchy mutation operator is proposed. Cauchy mutation operator enhances the exploration ability of the algorithm and improves the diversity of population. The proposed LI-CBBO method for image matching inherits both the advantages of CBBO and lateral inhibition mechanism. Series of comparative experiments using Particle Swarm Optimization (PSO), LI-PSO, BBO and LI-BBO have been conducted to demonstrate the feasibility and effectiveness of the proposed LI-CBBO.     

基于混合生物地理优化的混沌系统参数估计

混沌系统的参数估计本质上是多维参数的优化问题. 结合和声搜索方法和对立学习机理, 提出一种混合生物地理优化算法, 用于解决混沌系统的参数估计问题. 利用对立学习机理增加初始群体的多样性, 并引入和声搜索以增强局部寻优能力, 从而提升整体寻优性能. 以典型混沌系统为例进行了未知参数估计的数值仿真, 结果验证了所提出混合生物地理优化方法的有效性和鲁棒性.     

Application of the edge of chaos in combinatorial optimization

Many problems in science, engineering and real life are related to the combinatorial optimization. However, many combinatorial optimization problems belong to a class of the NP-hard problems, and their globally optimal solutions are usually difficult to solve. Therefore, great attention has been attracted to the algorithms of searching the globally optimal solution or near-optimal solution for the combinatorial optimization problems. As a typical combinatorial optimization problem, the traveling salesman problem(TSP) often serves as a touchstone for novel approaches. It has been found that natural systems, particularly brain nervous systems, work at the critical region between order and disorder, namely,on the edge of chaos. In this work, an algorithm for the combinatorial optimization problems is proposed based on the neural networks on the edge of chaos(ECNN). The algorithm is then applied to TSPs of 10 cities, 21 cities, 48 cities and 70 cities. The results show that ECNN algorithm has strong ability to drive the networks away from local minimums.Compared with the transiently chaotic neural network(TCNN), the stochastic chaotic neural network(SCNN) algorithms and other optimization algorithms, much higher rates of globally optimal solutions and near-optimal solutions are obtained with ECNN algorithm. To conclude, our algorithm provides an effective way for solving the combinatorial optimization problems.     

A Hybrid Rao-NM Algorithm for Image Template Matching

This paper proposes a hybrid Rao-Nelder–Mead (Rao-NM) algorithm for image template matching is proposed. The developed algorithm incorporates the Rao-1 algorithm and NM algorithm serially. Thus, the powerful global search capability of the Rao-1 algorithm and local search capability of NM algorithm is fully exploited. It can quickly and accurately search for the high-quality optimal solution on the basis of ensuring global convergence. The computing time is highly reduced, while the matching accuracy is significantly improved. Four commonly applied optimization problems and three image datasets are employed to assess the performance of the proposed method. Meanwhile, three commonly used algorithms, including generic Rao-1 algorithm, particle swarm optimization (PSO), genetic algorithm (GA), are considered as benchmarking algorithms. The experiment results demonstrate that the proposed method is effective and efficient in solving image matching problems.     

自适应混沌蚁群径向分析算法求解重力辅助导航匹配问题

针对现有重力导航匹配算法的匹配精度、匹配率受惯导初始位置误差影响较大以及实时性较差等不足,提出了一种基于自适应混沌蚁群径向分析的实时重力辅助导航匹配算法,新算法引入改进的连续域蚁群算法进行优化模型求解,通过进行连续域蚁群算法的信息素的自适应调整,同时对蚁群算法的搜索策略、计算参数、局部信息素进行混沌自适应处理,最终达到提高算法搜索效率、匹配率、抗噪性能的效果,实验结果表明,新算法对惯导初始误差不敏感,匹配率高,实时性强。     

车载图像跟踪系统中复杂场景下目标提取算法的研究

提出一种新的复杂场景下的目标图像提取方法。给出一种改进的Snake模型,并将其应用到初始模板的建立中;引入分形布朗随机场模型,利用小波分形维数和分形拟合误差确定可能的目标区域;定义了一种新的最小失配距离（MMD）相似性度量,并基于目标的特征区域进行快速相关匹配。该算法通过精确建立初始模板和采用由粗到精的目标搜索策略,既保证了目标提取的精度,又大大减少了计算量。     

一种改进的红外图像归一化互相关匹配算法

分析了传统归一化互相关算法在红外空中目标匹配定位时失效的原因,提出一种改进的红外图像归一化互相关匹配算法.该方法将模板和匹配区域之间的纹理相关计算看作一个最优化问题,寻求使图像纹理相关匹配鲁棒性最好的相关基准值,用图像的相关基准函数替代传统方法中的区域均值部分,构造了一种适用于的红外目标匹配的归一化相关算法.实验结果表明,该相关匹配算法对模板中背景部分的变化和非均匀性亮度变化有良好的抗干扰能力,较好地解决了恶劣环境下红外对空目标跟踪中匹配定位出错的问题.     

Biological image processing via Chaotic Differential Search and lateral inhibition

In this paper, we propose a hybrid biological image processing approach, which is based on Chaotic Differential Search (CDS) algorithm and lateral inhibition (LI) mechanism. We named this hybrid biological image processing approach as LI-CDS. Differential Search (DS) algorithm is a new bio-inspired optimization algorithm mimicking the migration behavior of an organism, and has been successfully used for solution of coordinate system transformation. The property of chaotic variable is integrated into DS to improve its search strategy so that it can escape from the local optimum. Furthermore, lateral inhibition mechanism, which is verified to have good effects on image edge extraction and image enhancement, is employed to pre-process images involved. In this hybrid biological image processing mechanism, our proposed LI-CDS method incorporates both advantages of chaos theory and lateral inhibition mechanism. Series of comparative experimental results by using LI-CDS, DS, CDS and Particle Swarm Optimization (PSO) demonstrate that the proposed LI-CDS performs better than the other three methods.     

Chaotically Temporal Retrieval of Memory

A neural network with nonlinear delays to produce temporal retrieval of memory is presented. In this network, chaotic motion of the local fields provides a mechanism for the system to escape from one memory to another. It is proved by numerical investigations that the chaotic temporal process can explore the topological structure of the state space and the system has better efficiency of searching global minimum of the energy function than the Hopfield model. The characters of the system show that it may have great potential use in solving combinatorial optimization problems with its complex dynamics.     

基于免疫双态微粒群的混沌系统自抗扰控制

利用人工免疫算法及粒子群优化算法融合的优点,提出了一种免疫双态微粒群算法(immune binary-state particle swarm optimization, IBPSO)的自抗扰控制器(IBPSO-ADRC),应用于混沌系统控制,构建一种混沌系统自抗扰控制系统.实验研究表明:该控制方法无需了解动态系统精确模型,具有响应速度快,有效抑制混沌系统参数摄动及较强抗干扰能力的特点. 关键词： 人工免疫系统 微粒群算法 混沌系统 自抗扰控制器     

Chaotic signal denoising algorithm based on sparse decomposition

Denoising of chaotic signal is a challenge work due to its wide-band and noise-like characteristics. The algorithm should make the denoised signal have a high signal to noise ratio and retain the chaotic characteristics. We propose a denoising method of chaotic signals based on sparse decomposition and K-singular value decomposition(K-SVD) optimization. The observed signal is divided into segments and decomposed sparsely. The over-complete atomic library is constructed according to the differential equation of chaotic signals. The orthogonal matching pursuit algorithm is used to search the optimal matching atom. The atoms and coefficients are further processed to obtain the globally optimal atoms and coefficients by K-SVD. The simulation results show that the denoised signals have a higher signal to noise ratio and better preserve the chaotic characteristics.     

基于原对偶状态转移算法的分数阶多涡卷混沌系统辨识

未知分数阶混沌系统参数辨识问题可转化为函数优化问题, 是实现分数阶混沌系统同步与控制的关键. 结合正交学习机制和原对偶学习策略, 提出一种原对偶状态转移算法, 用于解决分数阶混沌系统的参数辨识问题. 利用正交学习机制产生较优的初始种群增加算法的收敛能力, 并引入原对偶操作增加状态在空间的搜索能力, 提高算法的寻优性能. 在有噪声和无噪声情况下以分数阶多涡卷混沌系统的参数辨识为研究对象进行仿真. 结果表明了该算法的有效性、鲁棒性和通用性.     

一类不确定离散混沌系统的自抗扰控制器与小脑神经网络并行优化控制

针对一类受扰不确定离散非线性混沌系统,提出了基于免疫动态微粒群优化策略的ADRC与CMAC神经网络并行控制方法(ADRC-CMAC).ADRC控制器抑制系统扰动,保证系统的稳定性;CMAC神经网络控制器实现前馈控制保证系统的控制精度和响应速度.利用动态免疫微粒群算法对ADRC-CMAC并行控制器参数进行全局优化.实验结果表明该控制方法具有较快系统的响应速度,较好的抗干扰能力,控制精度高. 关键词： 自抗扰控制器 小脑神经网络 并行控制 混沌系统     

一种基于遗传算法的混沌系统参数估计方法

通过构造一个适当的适应度函数,将混沌系统的参数估计问题转化为一个参数的寻优问题,然后利用遗传算法的全局优化搜索能力对其进行求解.以典型的Lorenz混沌系统为例进行了数值模拟.实际数值模拟表明,使用这种方法可以有效地对混沌系统的参数进行估计 关键词： 混沌系统 参数估计 遗传算法     

数字图像相关技术的综合算法及其在断裂力学中的应用

鉴于散斑图的随机噪声和相关搜索运算过程仍是当今影响相关图像技术所获结果精度与计算速度两大有待研究解决的主要问题,提出一种基于小波变换、序惯相似度检测和统计相关算法三者相结合的新算法。其基本原理是应用小波变换对变形前后的散斑图进行滤波平滑处理;利用序惯相似度检测算法进行粗搜索,找到可能的匹配点;在可能的匹配点应用统计相关法进行细搜索,最终找到匹配点的位置。基本实验、计算和应用表明,这种算法在消除噪声和提高运算速度等方面,取得了良好的效果。     

一种热集成系统的动态多智能体微分进化算法

针对热集成系统换热网络存在的严重非凸非线性与多维多极值问题,提出动态多智能体微分进化算法.结合动态更新策略,并引入多智能体算法的环境感知能力,改进微分进化算法的种群生成方式与变异机制,并增强在大规模复杂非线性系统中的全局搜索能力.通过10SP2与9SP1换热网络经典算例优化,得到最佳年综合费用,体现出了改进算法更优的全局搜索能力.     

Cognitive radio adaptation for power consumption minimization using biogeography-based optimization

Adaptation is one of the key capabilities of cognitive radio, which focuses on how to adjust the radio parameters to optimize the system performance based on the knowledge of the radio environment and its capability and characteristics.In this paper, we consider the cognitive radio adaptation problem for power consumption minimization. The problem is formulated as a constrained power consumption minimization problem, and the biogeography-based optimization(BBO) is introduced to solve this optimization problem. A novel habitat suitability index(HSI) evaluation mechanism is proposed,in which both the power consumption minimization objective and the quality of services(Qo S) constraints are taken into account. The results show that under different Qo S requirement settings corresponding to different types of services, the algorithm can minimize power consumption while still maintaining the Qo S requirements. Comparison with particle swarm optimization(PSO) and cat swarm optimization(CSO) reveals that BBO works better, especially at the early stage of the search, which means that the BBO is a better choice for real-time applications.     

一类求解箱式约束优化问题的自适应引力搜索算法

为了改进引力搜索算法求解箱式约束优化问题的性能,提出了一类自适应引力搜索算法,新算法定义了算法停滞系数,当算法陷入停滞时,可以自适应的修改引力参数,帮助算法跳出停滞状态;定义了个体相似系数,当种群陷入局部最优时,通过变异策略改善种群的多样性。数值试验结果表明,新算法有效的平衡了全局开发和局部搜索能力,具有更强的全局寻优能力,适于求解复杂优化问题。     

量子算法的NMR实现方法

量子计算机是一种以量子耦合方式进行信息处理的装置[1 ] 。原则上 ,它能利用量子相干干涉方法以比传统计算机更快的速度进行诸如大数的因式分解、未排序数据库中的数据搜索等工作[2 ] 。建造大型量子计算机的主要困难是噪音、去耦和制造工艺。一方面 ,虽然离子陷阱和光学腔实验方法大有希望 ,但这些方法都还没有成功实现过量子计算。另一方面 ,因为隔离于自然环境 ,核自旋可以成为很好的“量子比特” ,可能以非传统方式使用核磁共振 (NMR)技术实现量子计算。本文介绍一种用NMR方法实现量子计算的方法 ,该方法能够用比传统方法少的步骤解决一个纯数学问题。基于该方法的简单量子计算机使用比传统计算机使用更少的函数“调用”判断一未知函数的类别。     

A chaotic quantum-behaved particle swarm optimization based on lateral inhibition for image matching

A novel Chaotic Quantum-behaved Particle Swarm Optimization Based on Lateral Inhibition (LI-CQPSO) is proposed in this paper, which is used to solve complicated image matching problems. As one of the meta-heuristic algorithms inspired by biological behaviors, Particle Swarm Optimization (PSO) has been successfully applied to image matching. However, high computational complexity and premature convergence of PSO are the main drawbacks that limit its further application. In this work, the proposed LI-CQPSO which combines advantages of chaos theory, quantum and lateral inhibition could have better performance. Chaos can guarantee the PSO escaping from local best, quantum can make the traditional PSO with better searching performance as well as having fewer parameters to control, and lateral inhibition is applied to extract the edge of the images by sharpening the spatial profile of excitation in response to a localized stimulus. The detailed process of LI-CQPSO is also given. The effectiveness and feasibility of the proposed algorithm are illustrated in solving image matching problems by series of comparative experiments with PSO, QPSO, and LI-PSO.