The :20Brain-state-in-a-box" Neural model is a gradient descent algorithm
Journal of Mathematical Psychology
Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Constructing force-closure grasps
International Journal of Robotics Research
Accelerated learning in layered neural networks
Complex Systems
Adaptive pattern recognition and neural networks
Adaptive pattern recognition and neural networks
International Journal of Robotics Research
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
A linear complementarity approach to the frictionless gripper problem
International Journal of Robotics Research
Machine learning with rule extraction by genetic assisted reinforcement (REGAR): application to nonlinear control
Mathematical Programming: Series A and B
Fundamentals of Artificial Neural Networks
Fundamentals of Artificial Neural Networks
Neurocontrollers trained with rules extracted by a genetic assisted reinforcement learning system
IEEE Transactions on Neural Networks
A fast tri-factorization method for low-rank matrix recovery and completion
Pattern Recognition
A Theoretical Approach of an Intelligent Robot Gripper to Grasp Polygon Shaped Objects
Journal of Intelligent and Robotic Systems
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This article presents a heuristic technique used for solving linear complementarity problems(LCP). Determination of minimum forces needed to firmly grasp an object by a multifingered robot gripper for different external force and finger positions is our proposed application. The contact type is assumed to be frictionless. The interaction in the gripper–object system is formulated as an LCP. A numerical algorithm (Lemke) is used to solve the problem [3]. Lemke is a direct deterministic method that finds exact solutions under some constraints. Our proposed neural network technique finds almost exact solutions in solvable positions, and very good solutions for positions that Lemke fails to find solutions. A new adaptive technique is used for training the neural network and it is compared with the standard technique. Mathematical analysis for the convergence of the proposed technique is presented.