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Potential Energy and Particle Interaction Approach for Learning in Adaptive Systems
[chapter]
2002
Lecture Notes in Computer Science
Adaptive systems research is mainly concentrated around optimizing cost functions suitable to problems. Recently, Principe et al. proposed a particle interaction model for information theoretical learning. In this paper, inspired by this idea, we propose a generalization to the particle interaction model for learning and system adaptation. In addition, for the special case of supervised multi-layer perceptron (MLP) training we propose the interaction force backpropagation algorithm, which is a
doi:10.1007/3-540-46084-5_74
fatcat:4drnippj55bgncfuoizjs2w3hm