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29 October 1996 Using external knowledge in neural network models
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One of the most important properties of neural networks is generality, as the same network can be trained to solve rather different tasks, depending on the training data. This is also one of the most prominent problems when practical real world problems are solved by neural networks, as existing domain knowledge is difficult to incorporate into the models. In this contribution we present methods for adding prior knowledge to neural network modeling. The approach is based on training the knowledge on the network of hard-coding the knowledge in advance to the connections or weights. The knowledge is specified as target values or constraints for different order partial derivatives of the network. This approach can be viewed as a flexible regularization method that controls directly the characteristics of the resulting mapping. The proposed algorithms have been implemented in a neural network modeling tool that supports modular network design and domain knowledge representation with fuzzy-like terms. In this paper we present examples of the effect of incorporating different degrees of information from the modular structure and the functional behavior of the target processes in the model building and training.
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Arto Selonen, Jouko Lampinen, and Leena Ikonen "Using external knowledge in neural network models", Proc. SPIE 2904, Intelligent Robots and Computer Vision XV: Algorithms, Techniques,Active Vision, and Materials Handling, (29 October 1996);

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