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| ASCII Text | x | ||
| Jacob Barhen, Sandeep Gulati, Michail Zak, "Neutral Learning of Constrained Nonlinear Transformations," Computer, vol. 22, no. 6, pp. 67-76, June, 1989. | |||
| BibTex | x | ||
| @article{ 10.1109/2.30722, author = {Jacob Barhen and Sandeep Gulati and Michail Zak}, title = {Neutral Learning of Constrained Nonlinear Transformations}, journal ={Computer}, volume = {22}, number = {6}, issn = {0018-9162}, year = {1989}, pages = {67-76}, doi = {http://doi.ieeecomputersociety.org/10.1109/2.30722}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - MGZN JO - Computer TI - Neutral Learning of Constrained Nonlinear Transformations IS - 6 SN - 0018-9162 SP67 EP76 EPD - 67-76 A1 - Jacob Barhen, A1 - Sandeep Gulati, A1 - Michail Zak, PY - 1989 VL - 22 JA - Computer ER - | |||
Two issues that are fundamental to developing autonomous intelligent robots, namely rudimentary learning capability and dexterous manipulation, are examined. A powerful neural learning formalism is introduced for addressing a large class of nonlinear mapping problems, including redundant manipulator inverse kinematics, commonly encountered during the design of real-time adaptive control mechanisms. Artificial neural networks with terminal attractor dynamics are used. The rapid network convergence resulting from the infinite local stability of these attractors allows the development of fast neural learning algorithms. Approaches to manipulator inverse kinematics are reviewed, the neurodynamics model is discussed, and the neural learning algorithm is presented.