標(biāo)題: Titlebook: Neural Networks in Robotics; George A. Bekey (Professor),Kenneth Y. Goldberg (A Book 1993 Springer Science+Business Media New York 1993 ex [打印本頁(yè)] 作者: 滲漏 時(shí)間: 2025-3-21 17:56
書目名稱Neural Networks in Robotics影響因子(影響力)
書目名稱Neural Networks in Robotics影響因子(影響力)學(xué)科排名
書目名稱Neural Networks in Robotics網(wǎng)絡(luò)公開度
書目名稱Neural Networks in Robotics網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Neural Networks in Robotics被引頻次
書目名稱Neural Networks in Robotics被引頻次學(xué)科排名
書目名稱Neural Networks in Robotics年度引用
書目名稱Neural Networks in Robotics年度引用學(xué)科排名
書目名稱Neural Networks in Robotics讀者反饋
書目名稱Neural Networks in Robotics讀者反饋學(xué)科排名
作者: CONE 時(shí)間: 2025-3-21 23:17
Hopfield Net Generation and Encoding of Trajectories in Constrained EnvironmentHopfield network transient dynamic is being exploited for resolving a path planning problem. A set of temporal trajectories which join two points, pass along others, avoid obstacles and be the shortest possible are discovered and encoded in the weights of the net.作者: 魯莽 時(shí)間: 2025-3-22 01:36 作者: FORGO 時(shí)間: 2025-3-22 08:09 作者: 驕傲 時(shí)間: 2025-3-22 08:56 作者: LARK 時(shí)間: 2025-3-22 16:54 作者: conference 時(shí)間: 2025-3-22 17:22
Control of Grasping in Robot Hands by Neural Networks and Expert Systems contrasted with control based on knowledge-based systems and neural networks. The development of an expert system for grasp control, which includes consideration of both object geometry and task, is described in detail. The chapter includes descriptions of neural network applications to the prediction of grasping in both human and robot hands.作者: Repetitions 時(shí)間: 2025-3-22 22:25
The Springer International Series in Engineering and Computer Sciencehttp://image.papertrans.cn/n/image/663721.jpg作者: 動(dòng)機(jī) 時(shí)間: 2025-3-23 01:49 作者: cajole 時(shí)間: 2025-3-23 07:08
Learning Global Topological Properties of Robot Kinematic Mappings for Neural Network-based Configuresulting in a global regularization of the ill-posed inverse problem.. Reasonable assumptions about the kinematic mapping, based on the physical properties of a robot arm, are exploited to learn the global topology of the mapping. Heuristic algorithms are applied to the wristless Puma 560 and the 3-作者: Hallmark 時(shí)間: 2025-3-23 11:53 作者: 敏捷 時(shí)間: 2025-3-23 15:26 作者: Oligarchy 時(shí)間: 2025-3-23 18:26 作者: 刺激 時(shí)間: 2025-3-23 23:07 作者: Atrium 時(shí)間: 2025-3-24 03:22
Some Preliminary Comparisons Between a Neural Adaptive Controller and a Model Reference Adaptive Conple and well defined first-order problem. We focus on the rate of convergence, and on the capacity to control non-linear and time-varying systems. Results from a first experiment show that the MRAC always converges faster and performs better for linear systems, but that its performances decline in c作者: 大洪水 時(shí)間: 2025-3-24 07:17
Stable Nonlinear System Identification Using Neural Network Models a stability theory approach to synthesizing and analyzing neural network based identification schemes. First static network architectures are combined with dynamical elements in the form of stable filters to construct a type of recurrent network configuration which is shown to be capable of approxi作者: Microgram 時(shí)間: 2025-3-24 12:19 作者: Circumscribe 時(shí)間: 2025-3-24 16:03
Neural Networks Learning Rules for Control: Uniform Dynamic Backpropagation, Heavy Adaptive Learningms. The Uniform Dynamic BackPropagation rule is based on a non regular optimization scheme (subgradient algorithm), and is devoted to the minimisation of a Min Max criterion, in a neural network synaptic matrix space. The Heavy Adaptive learning rule is a continuous hebbian learning rule, enabling a作者: 摻假 時(shí)間: 2025-3-24 20:35
Parameter Learning and Compliance Control Using Neural Networks [.], the shortcomings of current control methods in dealing with such applications were elucidated, and a new robust control approach based on terminal sliding modes was introduced. In this paper, the problem of identifying uncertain environments for stable contact control is considered. For the pu作者: 謊言 時(shí)間: 2025-3-25 01:50 作者: Antioxidant 時(shí)間: 2025-3-25 05:58 作者: LARK 時(shí)間: 2025-3-25 10:57 作者: 磨碎 時(shí)間: 2025-3-25 14:22 作者: Tincture 時(shí)間: 2025-3-25 17:14
Shahriar Najand,Zhen-Ping Lo,Behnam Bavarian the emphasis is placed on the technical aspects of these systems rather than on theoretical concepts. Lengthy mathematicalderivationshavebeenavoidedbecausethetheoryisnottreatedasanend initself,butratherservestoexplaintheexperimentalresultsobservedinthelaboratory. However, there is suf?cient theoret作者: fodlder 時(shí)間: 2025-3-25 21:20 作者: 山間窄路 時(shí)間: 2025-3-26 00:40
S. T. Venkataraman,S. Gulati,J. Barhen,N. Toomarianion, and practical problems. The title Solid-State Laser Engineering has been chosen because tbe emphasis is placed on engineering and practical considerations of solid-state Iasers. I have tried to enhance tbe description of the engineering aspects of Iaser construction and operation by including n作者: 斗志 時(shí)間: 2025-3-26 06:33 作者: 含水層 時(shí)間: 2025-3-26 09:26
Barak A. Pearlmutternteresting design concepts. However, in recent years the technology has matured to a point where solid-state Iasers have reached a plateau in their development. To a major extent, the growth in importance of solid-state Iasers for industrial and military applications and as a general research tool i作者: 預(yù)示 時(shí)間: 2025-3-26 15:16
0893-3405 for this purposewithout explicit programming. Some of the computational advantages andproblems of this approach are also presented. .For any serious student of 978-1-4613-6394-1978-1-4615-3180-7Series ISSN 0893-3405 作者: AROMA 時(shí)間: 2025-3-26 16:47
Ben J. A. Kr?se,P. Patrick van der Smagt,Frans C. A. Groen作者: 無能性 時(shí)間: 2025-3-27 00:05
Hugues Bersini,Luis Gonzalez Sotelino,Eric Decossaux作者: 反對(duì) 時(shí)間: 2025-3-27 01:29
Marco Saerens,Alain Soquet,Jean-Michel Renders,Hugues Bersini作者: 水汽 時(shí)間: 2025-3-27 08:52
A. G. Chassiakos,E. B. Kosmatopoulos,M. A. Christodoulou作者: 盡管 時(shí)間: 2025-3-27 09:30
Learning Global Topological Properties of Robot Kinematic Mappings for Neural Network-based Configur be globally regularized, and both the forward and inverse mappings can be completely learned. A non-linear function approximator such as a neural network can then be used to provide a solution to the inverse kinematics problem, allowing configuration control at a logical level.作者: 流動(dòng)性 時(shí)間: 2025-3-27 13:41
Some Preliminary Comparisons Between a Neural Adaptive Controller and a Model Reference Adaptive Conly, could be more appropriate in the case of control of that much has to be done to study the conditions of convergence of networks, in order to understand what can be really done by neural nets in non-linear control.作者: Bricklayer 時(shí)間: 2025-3-27 20:20 作者: allergen 時(shí)間: 2025-3-28 00:57 作者: CHASM 時(shí)間: 2025-3-28 03:31
Nicolas Seube as possible. Phenomenological descriptions using models were preferred to an abstract matbematical presenta- tion, even tbough many simplifications bad tben to978-3-662-14105-2Series ISSN 0342-4111 Series E-ISSN 1556-1534 作者: 鍍金 時(shí)間: 2025-3-28 07:12 作者: 宏偉 時(shí)間: 2025-3-28 12:32 作者: 違反 時(shí)間: 2025-3-28 17:23
George A. Bekey (Professor),Kenneth Y. Goldberg (A作者: 不規(guī)則 時(shí)間: 2025-3-28 19:06 作者: 補(bǔ)充 時(shí)間: 2025-3-29 01:07 作者: 神刊 時(shí)間: 2025-3-29 03:58
S. T. Venkataraman,S. Gulati,J. Barhen,N. Toomarianllege textbook, tbe book might be used in an advanced college course form on Iaser technology. The aim was to present the subject as clearly as possible. Phenomenological descriptions using models were preferred to an abstract matbematical presenta- tion, even tbough many simplifications bad tben to作者: Obsessed 時(shí)間: 2025-3-29 10:49 作者: 拋棄的貨物 時(shí)間: 2025-3-29 11:26 作者: CURT 時(shí)間: 2025-3-29 18:33 作者: expansive 時(shí)間: 2025-3-29 20:35 作者: 灰姑娘 時(shí)間: 2025-3-30 03:04
A One-eyed Self Learning Robot Manipulator 3D information from a single camera: a) using the size of the target object and b) using information from a sequence of images from the moving camera. In both cases a neural network is trained to perform the desired mapping.作者: Sad570 時(shí)間: 2025-3-30 05:12
Neurocontroller Selective Learning from Man-in-the-Loop Feedback Control Actionsstigate modeling the human operator with a three layer perceptron and an adaptive critic like selective learning. In each section several simulations are presented to clarify the objectives of the paper.作者: Omniscient 時(shí)間: 2025-3-30 10:29
Stable Nonlinear System Identification Using Neural Network Modelsmating a large class of dynamical systems. Identification schemes, based on neural network models, are then developed using the Lyapunov synthesis approach with the projection modification method. These identification schemes are shown to guarantee stability of the overall system, even in the presence of modeling errors.作者: 付出 時(shí)間: 2025-3-30 14:14 作者: EPT 時(shí)間: 2025-3-30 18:54 作者: 鎮(zhèn)壓 時(shí)間: 2025-3-31 00:40
