Editorial to special issue: Biomedical engineering: information processing, modeling, and control

Abstract

The Seventh International Symposium on Neural Networks (ISNN 2010) was held in Shanghai, China, from June 6 and 9, 2010. The ISNN 2010 was a great success and covered broad topics of neural network research and applications in diverse fields. As a continuation of this successful conference, we organized this special issue of Neural Computing and Applications on ‘‘Biomedical engineering: information processing, modeling, and control’’, with 10 selected highquality papers from ISNN 2010. The theme of this special issue is to present the state-ofthe-art developments in recent research focusing on neural networks for various biomedical engineering problems. With the increase of importance of the biomedical research, many interesting theories and applications in this challenging field have been studied over the past years. Meanwhile, neural networks have shown their powerful capacities in the learning and control field. It is reasonable to believe that the new theory, methodology, and tools for biomedical researches can be developed because of the advance of neural network study. To this end, the 10 selected papers in this special issue can be categorized into two major sections under this theme. The first section is focused on the neural network–based modeling and control for biomedical engineering. For instance, M. Amiri et al. studied the modeling problem of a severe neurological disease, epilepsy. They obtained a modified biologically inspired neural model for the epilepsy by integrating the functional role of astrocytes in the regulation of synaptic transmission. With this modified model, it is demonstrated that the healthy astrocytes are capable to compensate the variation of cortical excitatory input. The paper by P. C. Francisco and W. Yu proposes a neural network–based control algorithm for the magnetic levitation platform. This platform can be inserted into the patient’s abdominal cavity to perform diagnosis or surgery. The proposed controller combines the neural control and the sliding mode control together, which can reduce the chattering phenomenon and ensure the finite time convergence. The paper by G. Chen et al. considers the stabilization problem of a neutral stochastic hybrid systems with time-varying delays, which can describe some meaningful phenomena in biology. Based on the Lyapunov functional approach and linear matrix inequality (LMI) techniques, the delay-dependent sufficient conditions for the existence of the non-fragile observer–based H1 controller are given. The paper by C. Mu et al. suggests a novel least square support vector machine (LS-SVM)–based internal model controller for the MIMO nonlinear discrete-time systems. LS-SVM is employed to approximate the inverse system by using the input–output data. The proposed method can overcome the problem of the imperfect mathematical model and enhance the system robustness. The paper by Q. Cheng and J. Cao considers the global synchronization problem for a class of complex networks with both discrete time delays and stochastic disturbances. Recently, the complex network has been proved to be a useful modeling approach for many biological and social behaviors. The major tools for analyzing the network synchronization are the delay-fractioning approach, the properties of Kronecker product, and stochastic analysis technique. The obtained Z.-G. Hou (&) L. Cheng M. Tan Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, P.O. Box 2728, 100190 Beijing, China e-mail: zengguang.hou@ia.ac.cn