Synchronization of Chaotic System Using a Brain-Imitated Neural Network Controller and Its Applications for Secure Communications

Abstract

This paper proposes a new hybrid algorithm for secure communication applications. The proposed algorithm includes a fuzzy brain emotional learning controller (FBELC), a recurrent cerebellar model articulation controller (RCMAC), and a robust compensator (RC). The main brain-imitated neural network controller is a combination of the RCMAC and the FBELC, which is a mathematical model that approximates the decision and emotional activity of a human brain. A fuzzy inference system is also merged into the FBELC to produce an efficient hybrid structure, then it is used for secure communication applications. The 3-dimensional (3D) Genesio chaotic system is used for audio and image secure communication systems to show the potency and performance of the proposed algorithm. In the first application, a new image encryption algorithm is proposed to enhance security for information transmission, then several standard images are applied for the chaotic synchronization of image secure communication. In the second application, the audio signal is embedded in a 3D chaotic trajectory, which is used as an encryption carrier signal, after using the proposed method for the decryption, the source signal can be retrieved. The comparisons of simulation results using security analyses and root mean square error for recent algorithms are performed to validate the performance and efficiency of the proposed hybrid algorithm. The simulation results point out that our algorithm can attain better synchronization performance, and achieve more efficient audio and image secure communications.

Keywords

• Fuzzy inference system
• brain emotional learning control
• recurrent cerebellar model articulation controller
• 3D chaotic systems
• audio secure communication
• image secure communication