Distributed Representations, Simple Recurrent Networks, And Grammatical Structure
Machine Learning - Connectionist approaches to language learning
Neural networks: algorithms, applications, and programming techniques
Neural networks: algorithms, applications, and programming techniques
Machine Learning - Special issue on robot learning
Analysis of Mechanisms and Robot Manipulators
Analysis of Mechanisms and Robot Manipulators
Robot Manipulators: Mathematics, Programming, and Control
Robot Manipulators: Mathematics, Programming, and Control
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
Techniques for Generating the Goal-Directed Motion of Articulated Structures
IEEE Computer Graphics and Applications
Inverse kinematics in robotics using neural networks
Information Sciences: an International Journal
A Course on the Design of Reliable Digital Systems
IEEE Transactions on Education
Advances in Engineering Software
Optimum robot manipulator path generation using differential evolution
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Contour Tracking of a Redundant Robot Using Integral Variable Structure Control with Output Feedback
Journal of Intelligent and Robotic Systems
A parallel neural network approach to prediction of Parkinson's Disease
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
Advances in Artificial Intelligence
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The solution of inverse kinematics problem of redundant manipulators is a fundamental problem in robot control. The inverse kinematics problem in robotics is the determination of joint angles for a desired cartesian position of the end effector. For the solution of this problem, many traditional solutions such as geometric, iterative and algebraic are inadequate if the joint structure of the manipulator is more complex. Furthermore, many neural network approaches have been done to this problem. But the neural network-based solutions are not much reliable due to the error at the end of learning. Therefore, a reliability-based neural network inverse kinematics solution approach has been presented, and applied to a six-degrees of freedom (dof) robot manipulator in this paper. The structure of the proposed method is based on using three networks designed parallel to minimize the error of the whole system. Elman network, which has a profound impact on the learning capability and performance of the network, is chosen and designed according to the proposed solution method. At the end of parallel implementation, the results of each network are evaluated using direct kinematics equations to obtain the network with best result.