An adaptive neurocontroller with modified chaotic neural networks

This paper presents an indirect adaptive neurocontroller using modified chaotic neural networks (MCNN) for nonlinear dynamic system. A modified chaotic neural networks model is presented for simplifying the traditional chaotic neural networks and enforcing dynamic characteristics. A new dynamic backpropagation learning method is also developed. The proposed MCNN paradigm is applied to the system identification of a MIMO system and an indirect adaptive neurocontroller. The simulation results show the MCNN has robust adaptability to nonlinear dynamic systems.