共查询到20条相似文献,搜索用时 15 毫秒
1.
Hiroshi Kinjo Eiho Uezato Sam Chau Duong Tetsuhiko Yamamoto 《Artificial Life and Robotics》2009,13(2):464-469
This article considers intelligent control for a class of nonholonomic systems using a neurocontroller (NC) and a genetic
algorithm (GA). First, we introduce the design of the NC with use of the GA, and then we apply the NC to control two typical
examples of nonholonomic systems: a hopping robot in the flight phase and a four-wheel vehicle. In order to verify the effectiveness
of the control system, the performance of the NC is investigated and also compared to that of the so-called direct gradient
descent control (DGDC) approach, which is able to utilize a GA with the same examples in the comparison. Simulations show
that the NC could achieve a competitive performance and control the nonholonomic systems effectively. Furthermore, the use
of the NN and GA provide a straightforward solution for the problem without the need of the chained form conversion.
This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January
31–February 2, 2008 相似文献
2.
Feature selection has always been a critical step in pattern recognition, in which evolutionary algorithms, such as the genetic algorithm (GA), are most commonly used. However, the individual encoding scheme used in various GAs would either pose a bias on the solution or require a pre-specified number of features, and hence may lead to less accurate results. In this paper, a tribe competition-based genetic algorithm (TCbGA) is proposed for feature selection in pattern classification. The population of individuals is divided into multiple tribes, and the initialization and evolutionary operations are modified to ensure that the number of selected features in each tribe follows a Gaussian distribution. Thus each tribe focuses on exploring a specific part of the solution space. Meanwhile, tribe competition is introduced to the evolution process, which allows the winning tribes, which produce better individuals, to enlarge their sizes, i.e. having more individuals to search their parts of the solution space. This algorithm, therefore, avoids the bias on solutions and requirement of a pre-specified number of features. We have evaluated our algorithm against several state-of-the-art feature selection approaches on 20 benchmark datasets. Our results suggest that the proposed TCbGA algorithm can identify the optimal feature subset more effectively and produce more accurate pattern classification. 相似文献
3.
In this paper we propose a new approach in genetic algorithm called distributed hierarchical genetic algorithm (DHGA) for optimization and pattern matching. It is eventually a hybrid technique combining the advantages of both distributed and hierarchical processes in exploring the search space. The search is initially distributed over the space and then in each subspace the algorithm works in a hierarchical way. The entire space is essentially partitioned into a number of subspaces depending on the dimensionality of the space. This is done in order to spread the search process more evenly over the whole space. In each subspace the genetic algorithm is employed for searching and the search process advances from one hypercube to a neighboring hypercube hierarchically depending on the convergence status of the population and the solution obtained so far. The dimension of the hypercube and the resolution of the search space are altered with iterations. Thus the search process passes through variable resolution (coarse-to-fine) search space. Both analytical and empirical studies have been carried out to evaluate the performance between DHGA and distributed conventional GA (DCGA) for different function optimization problems. Further, the performance of the algorithms is demonstrated on problems like pattern matching and object matching with edge map. 相似文献
4.
Yi-Chung Hu 《Information Sciences》2010,180(13):2528-76
The analytic network process (ANP) is a useful technique for multi-attribute decision analysis (MCDA) that employs a network representation to describe interrelationships between diverse attributes. Owing to effectiveness of the ANP in allowing for complex interrelationships between attributes, this paper develops an ANP-based classifier for pattern classification problems with interdependence or independence among attributes. To deal with interdependence, this study employs genetic algorithms (GAs) to automatically determine elements in the supermatrix that are not easily user-specified, to find degrees of importance of respective attributes. Then, with the relative importance for each attribute in the limiting supermatrix, the current work determines the class label of a pattern by its synthetic evaluation. Experimental results obtained by the proposed ANP-based classifier are comparable to those obtained by other fuzzy or non-fuzzy classification methods. 相似文献
5.
6.
Takaaki Yamada Keigo Watanabe Kazuo Kiguchi Kiyotaka Izumi 《Artificial Life and Robotics》2002,6(3):113-119
The rings event is part of men's apparatus gymnastics. The ring exercise have free-floating characteristics of the gripping
point. Here, we propose a novel “rings gymnastic robot” aimed at an application in gymnastic coaching. The purpose of this
article is to develop fuzzy control rules to realize performances on the rings by considering torque minimization by genetic
algorithms (GAs), because these rules should be useful in coaching. The effectiveness of the controllers obtained is illustrated
by a simulation.
This work was presented, in part, at the Sixth International Symposium on Artificial Life and Robotics, Tokyo, Japan, January
15–17, 2001 相似文献
7.
为解决跳频信号压缩感知重构中稀疏度未知和稀疏字典规模庞大的问题,提出了一种基于多峰值匹配的压缩感知重构算法。该算法借鉴传统匹配追踪类算法结构,采用多峰值匹配原则进行原子选择,通过一次迭代确定候选集,然后利用回溯思想对候选集进行二次筛选获得支撑集,实现了跳频信号的精确重构。仿真结果表明,该算法重构性能与传统正交匹配追踪算法相近,同时重构速度大大提高。 相似文献
8.
In order to enhance integration between CAD and robots, wer propose a scheme to plan kinematically feasible paths in the presence
of obstacles based on task requirements. Thus, the feasibility of a planned path from a CAD system is assured before the path
is sent for execution. The proposed scheme uses a heuristic approach to deal with a rather complex search space, involving
high-dimensional C-space obstacles and task requirements specified in Cartesian space. When the robot is trapped by the local
minimum in the potential field related to the heuristic, a genetic algorithm is then used to find a proper intermediate location
that will guide it to escape out of the local minimum. For demonstration, simulations based on using a PUMA-typed robot manipulator
to perform different tasks in the presence of obstacles were conducted. The proposed scheme can also be used for mobile robot
planning.
The paper falls into Category (5). Please address correspondence to the second author. This work was supported in part by
the National Science Council, Taiwan, R.O.C., under grant NSC 82-0422-E-009-403. 相似文献
9.
Chi Kin Chow Author Vitae Hung Tat Tsui Author Vitae Author Vitae 《Pattern recognition》2004,37(1):105-117
Robust and fast free-form surface registration is a useful technique in various areas such as object recognition and 3D model reconstruction for animation. Notably, an object model can be constructed, in principle, by surface registration and integration of range images of the target object from different views. In this paper, we propose to formulate the surface registration problem as a high dimensional optimization problem, which can be solved by a genetic algorithm (GA) (Genetic Algorithms in Search Optimization and Machine Learning, Addison-Wesley, Reading, MA, 1989). The performance of the GA for surface registration is highly dependent on its speed in evaluating the fitness function. A novel GA with a new fitness function and a new genetic operator is proposed. It can compute an optimal registration 1000 times faster than a conventional GA. The accuracy, speed and the robustness of the proposed method are verified by a number of real experiments. 相似文献
10.
Nowadays, many traffic accidents occur due to driver fatigue. Driver fatigue detection based on computer vision is one of
the most hopeful applications of image recognition technology. There are several factors that reflect driver's fatigue. Many
efforts have been made to develop fatigue monitoring, but most of them focus on only a single behavior, a feature of the eyes,
or a head motion, or mouth motion, etc. When fatigue monitoring is implemented on a real model, it is difficult to predict
the driver fatigue accurately or reliably based only on a single driver behavior. Additionally, the changes in a driver's
performance are more complicated and not reliable. In this article, we represent a model that simulates a space in a real
car. A web camera as a vision sensor is located to acquire video-images of the driver. Three typical characteristics of driver
fatigue are involved, pupil shape, eye blinking frequency, and yawn frequency. As the influences of these characteristics
on driver fatigue are quite different from each other, we propose a genetic algorithm (GA)-based neural network (NN) system
to fuse these three parameters. We use the GA to determine the structure of the neural network system. Finally, simulation
results show that the proposed fatigue monitoring system detects driver fatigue probability more exactly and robustly.
This work was presented in part at the 11th International Symposium on Artificial Life and Robotics, Oita, Japan, January
23–25, 2006 相似文献
11.
Jeong-Jung Kim Jun-Woo Lee Ju-Jang Lee 《International Journal of Control, Automation and Systems》2009,7(3):447-457
A parameter search for a Central Pattern Generator (CPG) for biped walking is difficult because there is no methodology to
set the parameters and the search space is broad. These characteristics of the parameter search result in numerous fitness
evaluations. In this paper, nonparametric estimation based Particle Swarm Optimization (NEPSO) is suggested to effectively
search the parameters of CPG. The NEPSO uses a concept experience repository to store a previous position and the fitness
of particles in a PSO and estimated best position to accelerate a convergence speed. The proposed method is compared with
PSO variants in numerical experiments and is tested in a three dimensional dynamic simulator for bipedal walking. The NEPSO
effectively finds CPG parameters that produce a gait of a biped robot. Moreover, NEPSO has a fast convergence property which
reduces the evaluation of fitness in a real environment.
Recommended by Editorial Board member Euntai Kim under the direction of Editor Jae-Bok Song.
Jeong-Jung Kim received the B.S. degree in Electronics and Information Engineering from Chonbuk National University in 2006 and the M.S.
degree in Robotics from Korea Advanced Institute of Science and Technology in 2008. He is currently working toward a Ph.D.
at the Korea Advanced Institute of Science and Technology. His research interests include biologically inspired robotics and
machine learning.
Jun-Woo Lee received the B.S. degree in Electronics, Electrical and Communication Engineering from Pusan National University in 2007.
He is currently working toward an M.S. in the Korea Advanced Institute of Science and Technology. His research interests include
swarm intelligence and machine learning.
Ju-Jang Lee was born in Seoul, Korea, in 1948. He received the B.S. and M.S. degrees from Seoul National University, Seoul, Korea, in
1973 and 1977, respectively, and the Ph.D. degree in Electrical Engineering from the University of Wisconsin, in 1984. From
1977 to 1978, he was a Research Engineer at the Korean Electric Research and Testing Institute, Seoul. From 1978 to 1979,
he was a Design and Processing Engineer at G. T. E. Automatic Electric Company, Waukesha, WI. For a brief period in 1983,
he was the Project Engineer for the Research and Development Department of the Wisconsin Electric Power Company, Milwaukee.
He joined the Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, in 1984,
where he is currently a Professor. In 1987, he was a Visiting Professor at the Robotics Laboratory of the Imperial College
Science and Technology, London, U.K. From 1991 to 1992, he was a Visiting Scientist at the Robotics Department of Carnegie
Mellon University, Pittsburgh, PA. His research interests are in the areas of intelligent control of mobile robots, service
robotics for the disabled, space robotics, evolutionary computation, variable structure control, chaotic control systems,
electronic control units for automobiles, and power system stabilizers. Dr. Lee is a member of the IEEE Robotics and Automation
Society, the IEEE Evolutionary Computation Society, the IEEE Industrial Electronics Society, IEEK, KITE, and KISS. He is also
a former President of ICROS in Korea and a Counselor of SICE in Japan. He is a Fellow of SICE and ICROS. He is an Associate
Editor of IEEE Transactions on Industrial Electronics and IEEE Transactions on Industrial Informatics. 相似文献
12.
周伟恒 《计算机测量与控制》2020,28(2):53-57
当前故障检测机器人受到超声波影响故障检测存在精准度低的问题,据此提出了基于遗传算法的机械设备故障检测机器人设计。采用AD500-1A型号传感器采集机械设备内外部数据信息,使用等效转换电路使机器人实时感知周围环境变化信息,并利用灵敏度高电子仪器实现机器人传感工作;使用2路200万数字网络高清摄像头,监视整个机械设备,获取机器人结构通信、管理和运动信息;将proGee0813型号芯片作为导航设备定位芯片,根据实际需求获取信号指令,并选定机器人行驶路径;通过Unity与UE4引擎虚拟现实硬件交互设备进行故障定位追踪;利用关节装置连接车轮前臂和上臂,实现不同磁铁吸附与脱离,依据机器人结构,完成机器人硬件结构设计。采用遗传算法确定导航适应度函数,通过机器人视频采集信息,设计预警功能,并利用机器人即时生成设备故障图像,依据实现流程,在超声避障功能支持下,完成机械设备故障检测。由实验结果可知,该机器人检测精准度最高可达到0.96,提高了机器人检测鲁棒性。 相似文献
13.
针对蛇形机器人采用的循环抑制CPG模型,为解决CPG控制模型中参数整定效率低、不稳定的问题,阐述基于CPG模型的蛇形搜救机器人控制系统总体方案的设计,提出一种基于遗传算法的CPG控制模型参数优化方法,实现链式CPG网络的节律输出。仿真实现蛇形搜救机器人各关节控制信号的有效输出,仿真结果表明,该方法具有高效、准确、稳定等优点,可有效应用于蛇形搜救机器人的步态控制。 相似文献
14.
Y.Y. Cha 《Robotics and Computer》1997,13(2):145-156
The local path-planning algorithm using a human's heuristic and a laser range finder which has an excellent resolution with respect to angular and distance measurements is presented for real-time navigation of a free-ranging mobile robot. The algorithm utilizes the human's heuristic by which the shortest path from the various pathways to the goal can be found, even though the path may not have been taken before. In this paper, the attractive potentials in each candidate pathway are calculated in terms of the angle between the goal and pathway direction, the pathway width, and the angle between pathway and previous heading direction of the mobile robot. Consequently, the mobile robot chooses the optimal path that has the maximum attractive potential among candidate pathways. The heuristic principles are applied to the path decision of the mobile robot such as forward open way, side open way and no way. Also, the effectiveness of the established path-planning algorithm is examined by computer simulation and experiment in a complex environment. 相似文献
15.
基于改进遗传算法的餐厅服务机器人路径规划 总被引:1,自引:0,他引:1
针对遗传算法(GA)易产生早熟现象和收敛速度慢的问题,提出了一种基于传统遗传算法(TGA)的改进遗传算法——HLGA,用于实际餐厅服务机器人的路径规划。首先,通过基于编辑距离的相似度方法对拟随机序列产生的初始种群进行优化;其次,采用自适应算法的改进交叉概率和变异概率调整公式,对选择操作后的个体进行交叉、变异操作;最后,计算具有安全性评价因子函数的个体适应度值,进一步对比、迭代得到全局最优解。理论分析和Matlab仿真表明,与TGA和基于个体相似度改进的自适应遗传算法(ISAGA)相比,HLGA的运行时间分别缩短了6.92 s和1.79 s,且规划的实际路径更具有安全性和平滑性。实验结果表明HLGA在实际应用中能有效提高路径规划质量,同时缩小搜索空间、减少规划时间。 相似文献
16.
基于混合遗传算法的工业机器人最优轨迹规划 总被引:1,自引:0,他引:1
为兼顾工业机器人工作效率与轨迹的平稳性,提出一种基于混合遗传算法的二次轨迹规划方案.通过最优时间轨迹规划得到最小执行时间,在最小执行时间内进行最优冲击轨迹规划,进而规划出一条既高效又平滑的运动轨迹.采用五次均匀B样条在关节空间进行快速插值,不仅保证了各关节速度和加速度连续性还保证了各关节冲击的连续性.连续平滑的冲击可以减少机械振动,延长机器人的工作寿命.选用PUMA560为对象进行仿真与实验,结果表明,该方案可以获得比较理想的机器人运动轨迹,所提出的混合遗传算法能有效提高全局寻优的性能和算法运行的稳定性. 相似文献
17.
18.
In this paper we propose a genetic algorithm (GA) for solving the DNA fragment assembly problem in a computational grid. The algorithm, which is named GrEA, is a steady-state GA which uses a panmitic population, and it is based on computing parallel function evaluations in an asynchronous way. We have implemented GrEA on top of the Condor system, and we have used it to solve the DNA assembly problem. This is an NP-hard combinatorial optimization problem which is growing in importance and complexity as more research centers become involved on sequencing new genomes. While previous works on this problem have usually faced 30 K base pairs (bps) long instances, we have tackled here a 77 K bps long one to show how a grid system can move research forward. After analyzing the basic grid algorithm, we have studied the use of an improvement method to still enhance its scalability. Then, by using a grid composed of up to 150 computers, we have achieved time reductions from tens of days down to a few hours, and we have obtained near optimal solutions when solving the 77 K bps long instance (773 fragments). We conclude that our proposal is a promising approach to take advantage of a grid system to solve large DNA fragment assembly problem instances and also to learn more about grid metaheuristics as a new class of algorithms for really challenging problems. 相似文献
19.
20.
Gelenbe has proposed a neural network, called a Random Neural Network, which calculates the probability of activation of the neurons in the network. In this paper, we propose to solve the patterns recognition problem using a hybrid Genetic/Random Neural Network learning algorithm. The hybrid algorithm trains the Random Neural Network by integrating a genetic algorithm with the gradient descent rule-based learning algorithm of the Random Neural Network. This hybrid learning algorithm optimises the Random Neural Network on the basis of its topology and its weights distribution. We apply the hybrid Genetic/Random Neural Network learning algorithm to two pattern recognition problems. The first one recognises or categorises alphabetic characters, and the second recognises geometric figures. We show that this model can efficiently work as associative memory. We can recognise pattern arbitrary images with this algorithm, but the processing time increases rapidly. 相似文献