A neural model for multi-expert architectures

Details A neural model for multi-expert architectures A neural model for multi-expert architectures

2002-02-19

arxiv.org/abs/nlin/0202039v1

Proceedings of the International Joint Conference on Neural Networks (IJCNN 2002), 2755-2760.

Nonlinear Sciences

Adaptation and Self-Organizing Systems

LaTeX, 8 pages, 5 figures

Scientific paper

We present a generalization of conventional artificial neural networks that allows for a functional equivalence to multi-expert systems. The new model provides an architectural freedom going beyond existing multi-expert models and an integrative formalism to compare and combine various techniques of learning. (We consider gradient, EM, reinforcement, and unsupervised learning.) Its uniform representation aims at a simple genetic encoding and evolutionary structure optimization of multi-expert systems. This paper contains a detailed description of the model and learning rules, empirically validates its functionality, and discusses future perspectives.

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