Abstract
In view of the shortcomings of traditional neural network in the actual information fusion process, a data anti-interference method based on self-organizing incremental learning neural network is proposed, which is used to cluster and represent the dynamic input data online without prior knowledge. In this paper, the neuron distribution, dynamic node adjustment, topology representation and denoising process of the network are described in detail. Finally, the multi-source heterogeneous data collected by the sensor can be self-adaptive dimensionality reduction and self-organization learning. At the same time, it has strong robustness to noise data, so that it can continuously learn new patterns in data flow. SOINN model is suitable for supervised learning, associative memory, pattern-based reasoning, manifold learning and other learning scenarios. It can also be extended to the application fields of unbalanced, highly complex and nonlinear big data prediction.
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