A SOM-based fuzzy system and its application in handwritten digit recognition

The paper presents a neuro-fuzzy system by using Kohonen's self-organizing feature map algorithm, not only for its vector quantization feature, but also for its topological property. This property prevents the proposed neuro-fuzzy system from suffering from a drawback like any of the conventional clustering algorithm based fuzzy systems, i.e. the optimal number of clusters or some kind of similarity threshold must be predetermined. Associated with the self-organizing feature map based fuzzy system is a hybrid learning algorithm, which is for initial parameter setting and fine-tuning the parameters of the system. Application of the proposed fuzzy systems in optical handwritten digit recognition is reported. High recognition rates can be achieved.

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