Neural networks have revolutionized the field of artificial intelligence, offering powerful tools for pattern recognition, classification, and regression tasks. Among the various types of neural networks, Product Unit Neural Networks (PUNNs) stand out due to their unique architecture, which enables them to model complex, non-linear relationships more effectively than traditional networks. A crucial aspect of understanding and optimizing these networks involves the analysis of their fitness landscapes. This mini review explores the concept of fitness landscape analysis in the context of PUNNs, examining its implications for network design, training efficiency, and overall performance.
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Journal of Applied & Computational Mathematics received 1282 citations as per Google Scholar report
