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Cascade hyperchaotic fuzzy system (CHCFS): discussions on accuracy and interpretability

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Abstract

In recent years, studies on chaotic fuzzy systems inspired by the human brain have been increased to construct a robust and flexible intelligent network. This paper presents an innovative architecture called cascade hyperchaotic fuzzy system (CHCFS) to dominate complications in the type-2 fuzzy systems and increase the chaotic performance of a universal framework, from the accuracy and interpretability point of view. The chaotic features of the proposed model including chaotic search and new information generation, make the structure capable of handling uncertainties. Chaotic search is done in a cascade structure based on two or more one-dimensional chaotic maps. Output of cascade structure provides a new chaotic map that has more high nonlinearity than its grain maps. Fusion of this structure into the membership layer of a conventional fuzzy system makes the CHCFS more capable of confronting nonlinear problems. Analyzing the bifurcation diagram of CHCFS and applying it to the problem of chaotic modeling, the new model is investigated. Simulation results demonstrate that compared with conventional fuzzy systems, and under different chaotic degrees, the proposed CHCFS provides more accurate, interpretable and robust results.

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datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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  1. Department of Computer Engineering, Damghan Branch, Islamic Azad University, Damghan, Iran

    Hamid Abbasi

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Abbasi, H. Cascade hyperchaotic fuzzy system (CHCFS): discussions on accuracy and interpretability. Evolving Systems 15, 153–170 (2024). https://doi.org/10.1007/s12530-023-09546-5

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