共查询到20条相似文献,搜索用时 15 毫秒
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Color image segmentation, an ill-posed problem, can be treated as a process of dividing a color image into some constituent regions and each region is homogeneous. In this study, a saliency-directed color image segmentation approach using “simple” modified particle swarm optimization (PSO) is proposed, in which both low-level features and high-level image semantics extracted from each color image are employed. To extract high-level image semantics from each color image, the visual attention saliency map for each color image is generated by three (color, intensity, and orientation) feature maps, which is used to guide region merging using “simple” modified PSO and a hybrid fitness function for color image segmentation. The proposed approach contains four stages, namely, color quantization, feature extraction, small region elimination, and region merging using “simple” modified PSO. Based on the experimental results obtained in this study, as compared with four comparison approaches, the proposed approach usually provides the better color image segmentation results. 相似文献
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Zhijin Zhao Shiyu Xu Shilian Zheng Junna Shang 《Wireless Communications and Mobile Computing》2009,9(7):875-881
One of the basic capabilities of cognitive radio is to adapt the radio parameters according to the changing environment and user needs. This paper proposes a new adaptation method which uses particle swarm optimization (PSO) to optimize cognitive radio parameters given a set of objectives. The procedure of the proposed method is presented and multicarrier system is used for simulation analysis. Experimental results show that the proposed method performs far better than genetic algorithm (GA)‐based adaptation method in terms of convergence speed, converged fitness values, and stability. The proposed method can also provide the tradeoffs of the objective functions, and the resulting parameter configuration is consistent with the weights of the objective functions. Copyright © 2008 John Wiley & Sons, Ltd. 相似文献
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Gholamreza Karimi Ali Lotfi 《AEUE-International Journal of Electronics and Communications》2013,67(8):723-728
In this paper, a novel pre-distorter is presented using the particle swarm optimization (PSO) for an RF power amplifier linearization has been presented. We used the PSO in order to design of an efficient pre-distorter for the linearization of the output of an RF power amplifier by using the output data of the proposed power amplifier. The PSO is implemented to estimate and optimize the coefficient parameters of the work function in the proposed pre-distorter block diagram. The proposed method using PSO is most efficient because this approach is independent of the output of the power amplifier. The proposed method has been simulated with two-tone input signal and output power spectrum has been compared, where the obtained adjacent channel leakage ration (ACLR) is better than 50 dBc for both channels. Therefore, a quite significant improvement in linearity is achieved. 相似文献
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This paper presents an optimized design of non-cross feed Printed Log Periodic Dipole Array (PLPDA) antenna using Particle Swarm Optimization (PSO) technique. An improved feed structure of non-cross fed dipoles is chosen as reference antenna, which avoids the complexity of conventional feeding with long coaxial line and CPW feed. A simple fitness function based on S11 parameter is used in PSO to achieve the goal of size reduction and bandwidth enhancement. Simulation results on CST software are verified by a manufactured prototype of proposed PSO optimized non-cross feed PLPDA antenna using FR 4 substrate with a thickness of 1.6 mm. The measured bandwidth of proposed antenna is 4.2–11.6 GHz with a fractional bandwidth of 93.6%, whereas the reference antenna covers the frequency range from 4.2 to 9.2 GHz with a fractional bandwidth of 74.6%. The effective area of the proposed design is 30% lesser than reference antenna. Proposed antenna is offering peak gain of 7.6 dBi with an average gain of 5.5 dBi in desired band. The electrical size of optimized structure is 0.53λ at center frequency. Thus, proposed antenna is offering higher bandwidth and significantly smaller size with less complexity and lower cost, while maintaining the log periodic nature and gain. 相似文献
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针对函数优化问题,提出了一种基于离差平方和法的粒子群优化算法。该算法用混沌序列初始化粒子的位置和速度,选择好于粒子群优化算法产生的粒子位置。通过离差平方和法进行聚类,利用分类方式来更新粒子的速度。最后将算法应用到3个典型的函数优化问题中,数值结果比较表明,提高了算法搜索能力,全局最优解的精度和收敛速度。 相似文献
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《AEUE-International Journal of Electronics and Communications》2014,68(6):540-549
Antenna arrays with high directivity and low side lobe levels need to be designed for increasing the efficiency of communication systems. A new evolutionary technique, cat swarm optimization (CSO), is proposed for the synthesis of linear antenna arrays. The CSO is a high performance computational method capable of solving linear and non-linear optimization problems. CSO is applied to optimize the antenna element positions for suppressing side lobe levels and for achieving nulls in desired directions. The steps involved in the problem formulation of the CSO are presented. Various design examples are considered and the obtained CSO based results are validated by comparing with the results obtained using particle swarm optimization (PSO) and ant colony optimization (ACO). The flexibility and ease of implementation of the CSO algorithm is evident from this analysis, showing the algorithm's usefulness in electromagnetic optimization problems. 相似文献
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将粒子群算法与迭代自组织数据分析算法(ISODATA)结合,提出了一种基于粒子群的ISODATA算法。仿真实验表明,利用该算法可以对电梯交通模式进行准确、实时的识别。 相似文献
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粒子群优化算法是模拟鸟类觅食行为思想的随机搜索算法,主要是通过迭代寻找最优解。将粒子随机初始化改进为固定初始化,并将动态分群思想引入粒子群优化算法将整个种群划分为三个子群,根据不同群中粒子的情况自适应地选择惯性权重,以此提高粒子的搜索能力。仿真实验结果表明,该方法大大提高了搜索过程中粒子的多样性,避免粒子陷入局部最优,提高了求解的速度和精度。 相似文献
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市车载网环境下车辆的高速移动以及街道障碍物阻挡等原因,导致VANETs分割现象严重,以至于车载网不能正常通信,因此许多研究提出通过引入无线接入点(AP)来增强车载网通信的可能性.本文就是针对城市环境的VANETs的AP布局问题的研究,在基于车流量和粒子群算法的基础上提出的解决方案,并给出了相应的仿真,仿真结果表明该算法能在保证覆盖率的情况下实现AP的优化布局,同时在寻优过程中具有较快的收敛速度和较好的收敛性. 相似文献
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Robust loop shaping-fuzzy gain scheduling control of a servo-pneumatic system using particle swarm optimization approach 总被引:2,自引:0,他引:2
In this paper, a new technique called robust loop shaping-fuzzy gain scheduled control (RLS-FGS) is proposed to design an effective nonlinear controller for a long stroke pneumatic servo system. In our technique, a nonlinear dynamic model of a long stroke pneumatic servo plant is identified by the fuzzy identification method and is used as the plant for our design. The structure of local controllers is selected as PID control which is proven by many research works that this type of control has many advantages such as simple structure, well understanding, and high performance. The proposed technique uses particle swarm optimization (PSO) to find the optimal local controllers which maximize the average stability margin. In addition, performance weighting function which is normally difficult to obtain is automatically determined by PSO. By the proposed technique, the RLS-FGS controller can be designed, and the structure of local controllers is still not complicated. As seen in the simulation and experimental results, our proposed technique is better than the classical gain scheduled PID controller tuned by pole placement and the conventional fuzzy PID controller tuned by ISE method in terms of robust performance. 相似文献
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微粒群优化算法在协同建筑设计中的应用 总被引:7,自引:0,他引:7
介绍了群体智能的特点、算法以及基于群体智能的多agent协同设计系统模型。重点介绍微粒群优化算法的原理,工作流程。最后,以一个建筑外观设计为实例,介绍了算法在协同建筑设计组装过程中的应用。 相似文献
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设计两种基于粒子群优化算法(PSO)和基于遗传算法(GA)的多输入多输出(MIMO)系统检测算法。提出一种新的融合GA和PSO进化机制的遗传粒子群进化(GPSO)算法,并将其应用于MIMO系统检测问题求解。新算法改善了初始化种群,并将每一代粒子划为精英粒子、次优粒子和糟糕粒子三部分,对这三种粒子分别采用极值扰动、PSO进化和淘汰策略以改善算法的全局和局部搜索能力,从而加快算法的寻优速率和收敛速度。仿真结果表明:与基于PSO和基于GA的检测算法相比,GPSO的检测算法能够很大程度减少种群规模和迭代次数。而与最优的最大似然译码算法相比,GPSO检测算法能够在计算复杂度和误码性能之间获得很好的折中。 相似文献
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为提高局部模糊聚类算法(WFLICM)对噪声图像 分割的抗噪性,克服模糊聚类图像分割算法对初 始聚类中心的敏感性及易陷入局部最优问题,在WFLICM算法的基础上提出一种基于粒子群 优化的融合 局部和非局部空间信息的模糊聚类图像分割算法(PSO-WMNLFCM)。首先,利用粒子群优化 算法的全局 寻优能力得到最优粒子,并以此粒子作为模糊聚类算法的初始聚类中心。其次,用像素的非 局部空间信息 替换模糊因子中的局部邻域值,产生新的目标函数。最后,由拉格朗日乘子法最小化目标函 数,得到隶属 度和聚类中心的更新公式,从而完成图像分割。仿真结果表明,PSO-WMNLFCM算法相比于 模糊局部聚 类(FLICM)算法、局部模糊权重(WFLICM)算法、非局部模糊聚类(NLFCM)算法、非局部模 糊聚类 (MNLFCM)算法、基于粒子 群的局部模糊聚类(PSO-FLICM)算法的划分系数提高了20.92%,20.51%,24.84%,1.44%,23.28%左右。 相似文献
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Adnan Ahmed Khan Sajid Bashir Muhammad Naeem Syed Ismail Shah Xiaodong Li 《International Journal of Communication Systems》2008,21(12):1239-1257
Symbol detection in multi-input multi-output (MIMO) communication systems using different particle swarm optimization (PSO) algorithms is presented. This approach is particularly attractive as particle swarm intelligence is well suited for real-time applications, where low complexity and fast convergence is of absolute importance. While an optimal maximum likelihood (ML) detection using an exhaustive search method is prohibitively complex, PSO-assisted MIMO detection algorithms give near-optimal bit error rate (BER) performance with a significant reduction in ML complexity. The simulation results show that the proposed detectors give an acceptable BER performance and computational complexity trade-off in comparison with ML detection. These detection techniques show promising results for MIMO systems using high-order modulation schemes and more transmitting antennas where conventional ML detector becomes computationally non-practical to use. Hence, the proposed detectors are best suited for high-speed multi-antenna wireless communication systems. Copyright © 2008 John Wiley & Sons, Ltd. 相似文献
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