No abstract

This paper considers competitive learning networks using three types of hard, soft, and fuzzy learning schemes. The hard competitive learning algorithm is with the winner-take-all. The soft competition learning algorithm is with a stochastic relaxation technique using the Gibbs distribution as a dynamic neighborhood function. The fuzzy competition learning algorithm is with a fuzzy relaxation technique using fuzzy membership functions as kernel type neighborhood interaction functions. Some numerical examples are made for these three types of competitive learning schemes. The numerical results show that the fuzzy learning has better performance than hard and soft learning under the normal mixture data. We then present an application to magnetic resonance image segmentation. A real case of ophthalmology recommended by a neurologist with MR image data is examined in this paper. These competitive learning algorithms are used in segmenting the ophthalmological MRI data for reducing medical image noise effects with a learning mechanism. Based on the segmentation results, the fuzzy learning gives better performance than hard and soft learning so that the fuzzy competitive learning algorithm is recommended for use in MRI segmentation as an aid for support diagnoses. C