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» A Framework for Grid-based Neural Networks
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IROS
2006
IEEE
97views Robotics» more  IROS 2006»
15 years 3 months ago
A New Method of Force Control for Unknown Environments
–We propose a new control technique for force control on unknown environments. In particular, the proposed approach overcomes the need for precise estimation of environment param...
Vishnu Mallapragada, Duygun Erol, Nilanjan Sarkar
CORR
2006
Springer
111views Education» more  CORR 2006»
14 years 9 months ago
An associative memory for the on-line recognition and prediction of temporal sequences
This paper presents the design of an associative memory with feedback that is capable of on-line temporal sequence learning. A framework for on-line sequence learning has been prop...
Joy Bose, Stephen B. Furber, Jonathan L. Shapiro
GECCO
2007
Springer
149views Optimization» more  GECCO 2007»
15 years 4 months ago
Division blocks and the open-ended evolution of development, form, and behavior
We present a new framework for artificial life involving physically simulated, three-dimensional blocks called Division Blocks. Division Blocks can grow and shrink, divide and fo...
Lee Spector, Jon Klein, Mark Feinstein
NEUROSCIENCE
2001
Springer
15 years 2 months ago
Biological Grounding of Recruitment Learning and Vicinal Algorithms in Long-Term Potentiation
Biological networks are capable of gradual learning based on observing a large number of exemplars over time as well as of rapidly memorizing specific events as a result of a sin...
Lokendra Shastri
IJCNN
2000
IEEE
15 years 2 months ago
Predictive Multiple Model Switching Control with the Self-Organizing Map
—A predictive, multiple model control strategy is developed by extension of self-organizing map (SOM) local dynamic modeling of nonlinear autonomous systems to a control framewor...
Mark A. Motter