Switched State Controlled-CNN: An Alternative Approach in Generating Complex Systems with Multivariable Nonlinearities Using CNN
INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS, cilt.28, sa.6, 2018 (SCI-Expanded)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 28 Sayı: 6
- Basım Tarihi: 2018
- Doi Numarası: 10.1142/s0218127418300197
- Dergi Adı: INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Anahtar Kelimeler: State Controlled-Cellular Neural Networks, multivariable nonlinearities, complex dynamics, CELLULAR NEURAL-NETWORKS, CIRCUIT
- Sivas Cumhuriyet Üniversitesi Adresli: Evet
Özet
It has been already shown in the literature that extended State Controlled-Cellular Neural Network (SC-CNN) could be used to approximate the behavior of complex dynamics with multivariable nonlinearities by modifying the nonlinearity. In this study, a different approach to imitating the multivariable nonlinearities in complex dynamical systems by switching the states of SC-CNN is given. For this purpose, first of all, Rossler type system dynamics are generated by switching the states of Cellular Neural Networks (CNNs). In the second study, Sprott-94-f type system is presented. All systems are numerically and experimentally investigated in order to verify the convenience of the standpoint.