This book offers a comprehensive guide to Fractional Discrete Neural Networks, delving into their mathematical foundations, stability, solvability, and chaotic dynamics. By combining rigorous analytical approaches with practical numerical simulations, it illuminates how fractional derivatives can enhance neural network modeling, particularly for systems influenced by memory and hereditary effects. Readers will discover cutting-edge methods for assessing network behavior and stability, along with robust simulation techniques. The book’s diverse applications span image and signal processing, pattern recognition, artificial intelligence, and data science, showcasing the potential of fractional models to outperform traditional methods in tasks requiring precision and adaptability.
