I.J. Information Technology and Computer Science, 2015, 02, 80-87 Published Online January 2015 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijitcs.2015.02.10 Copyright © 2015 MECS I.J. Information Technology and Computer Science, 2015, 02, 80-87 Synchronization New 3D Chaotic System Using Brain Emotional Learning Based Intelligent Controller Masoud Taleb Ziabari Faculty of Engineering, Computer Engineering Group, Mehr Aeen University, Bandar Anzali, Iran Email: m.t.ziabari@gmail.com Ali Reza Sahab Faculty of Engineering, Electrical Engineering Group, Islamic Azad University, Lahijan Branch, Iran Email: sahab@liau.ac.ir Seyedeh Negin Seyed Fakhari Department of Electrical & Computer Science, KadousInstiute of Higher Education,Rasht, Iran Email: n_s_fakhari@yahoo.com Abstract— One of the most important phenomena of some systems is chaos which is caused by nonlinear dynamics. In this paper, the new 3 dimensional chaotic system is firstly investigated and then utilizing an intelligent controller which based on brain emotional learning (BELBIC), this new chaotic system is synchronized. The BELBIC consists of reward signal which accept positive values. Improper selection of the parameters causes an improper behavior which may cause serious problems such as instability of system. It is needed to optimize these parameters. Genetic Algorithm (GA), Cuckoo Optimization Algorithm (COA), Particle Swarm Optimization Algorithm (PSO) and Imperialist Competitive Algorithm (ICA) are used to compute the optimal parameters for the reward signal of BELBIC. These algorithms can select appropriate and optimal values for the parameters. These minimize the Cost Function, so the optimal values for the parameters will be founded. Selected cost function is defined to minimizing the least square errors. Cost function enforce the system errors to decay to zero rapidly. Numerical simulation results are presented to show the effectiveness of the proposed method. Index Terms— New 3D Chaotic System, Synchronization, BELBIC, Genetic Algorithm, Cuckoo Optimization Algorithm, Particle Swarm Optimization Algorithm, Imperialist Competitive Algorithm, Cost Function I. INTRODUCTION Chaos synchronization, an important topic in nonlinear science, has been developed and studied extensively in the last few years due to its potential application to physics, chemical reactor, biomedical and secure communications. Generally the two chaotic systems in synchronization are called drive system and response system, respectively. The idea of synchronization is to use the output of the drive system to control the response system and make the output of the response system follow the output of the drive system. Chaos synchronization has attracted a great deal of attention ever since Pecora and Carroll [1] established a chaos synchronization scheme for two identical chaotic systems with different initial conditions. Many methods for chaos synchronization have been proposed, such as, Robust Control [2], the sliding method control [3], linear and nonlinear feedback control [4], function projective [5,6], adaptive control [7], active control [8], backstepping control [9], generalized backsteppig method control [10] and so on. But many above-mentioned methods can only applied some given chaotic system, some methods will produce the singularity problem in synchronizing the chaotic system and most of the methods in the literatures need more than one variable information of the master system. In parallel with industrial and technological improvement, control systems and their control methods have become sophisticated. Control of new systems using previous old methods has become difficult. Further, considering human brain patterns and abilities in order to control and solve problems has resulted in emergence of new intelligent controlling methods which utilizes human brain operation patterns which are mentioned in following. Brain Emotional Learning Based Intelligent Controller (BELBIC) was introduced for the first time by Lucas in 2004 [11]. Brain Emotional Learning Based Intelligent Controller (BELBIC) is an example of bioinspired control methods which is based on limbic system of mammalian brain. This controller is based on emotional behaviors in biological systems. Emotion is an emergent behavior in biological systems for fast decision making in complex environments. The advantages of this behavior cannot be neglected in creature survival [12]. During the past few years, the BELBIC has been used in control devices for several industrial applications. The BELBIC has been successfully employed for making decisions and controlling linear systems and nonlinear systems such as, Brain Emotional Learning Intelligent Controller (BELBIC) for the control of two benchmark nonlinear plants was applied in [13]. In [14], a problem of