Abstract
This chapter provides a thorough review of the classical algorithms on unsupervised neural learning. It begins with a brief introduction to recurrent neural topology and then presents in detail both binary and continuous Hopfield nets, their stability analysis and applications. The chapter also presents a detailed overview to adaptive resonance theory and its application in solving the ’stability plasticity conflict’ problem in classical pattern recognition. Finally, the chapter introduces fuzzy associative memory neural nets and outlines algorithms for pattern classification by the proposed neural nets. Concluding remarks are listed at the end of the chapter.
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(2005). Unsupervised Neural Learning Algorithms. In: Computational Intelligence. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-27335-2_9
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DOI: https://doi.org/10.1007/3-540-27335-2_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20898-3
Online ISBN: 978-3-540-27335-6
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