Dynamic structure adaptation in feedforward neural networks-an example of plant monitoring

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In the paper artificial neural networks are introduced which are capable of adapting their structure in response to changes in the environment. Feedforward neural networks with multi-layer architecture were trained by modified backpropagation algorithm with forgetting of the connection weights. The applied training algorithm results in a skeleton network structure which can be used for knowledge acquisition. In the authors' algorithm, the decayed weights are not deleted but fluctuate around zero with a magnitude proportional to the rate of forgetting. Small fluctuations of the weights can grow into a structural evolution in the neural net if properties of the input clusters change. This feature is especially advantageous to on-line system monitoring applications when a rigid neural network structure could lead to mis-interpretation of measurements among dynamically changing conditions. Structural adaptation features and improved generalization capability of the proposed method are illustrated using an example of system state identification in a nuclear reactor.

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