Dynamic Optimal Training for Competitive Neural Networks
Keywords:Competitive neural networks, unsupervised learning, clustering, pattern classification, image compression
AbstractThis paper introduces an unsupervised learning algorithm for optimal training of competitive neural networks. The learning rule of this algorithm is rived from the minimization of a new objective criterion using the gradient descent technique. Its learning rate and competition difficulty are dynamically adjusted throughout iterations. Numerical results that illustrate the performance of this algorithm in unsupervised pattern classification and image compression are also presented, discussed, and compared to those provided by other well-known algorithms for several examples of real test data.
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How to Cite
Madiafi, M., & Bouroumi, A. (2014). Dynamic Optimal Training for Competitive Neural Networks. COMPUTING AND INFORMATICS, 33(2), 237–258. Retrieved from https://www.cai.sk/ojs/index.php/cai/article/view/544