Global robust stabilization of feedforward systems with uncertainties

This paper studies the global robust stabilization problem for a class of feedforward systems that is subject to both dynamic and time-varying static uncertainties. A small gain theorem-based bottom-up recursive design is developed for constructing a nested saturation control law. At each recursion, two versions of small gain theorem with restrictions are employed to establish the global attractiveness and local stability of the closed-loop system at the equilibrium point, respectively.

On convergence of covariance matrix of empirical Bayes hyper-parameter estimator

Regularized system identification has become the research frontier of system identification in the past decade.One related core subject is to study the convergence properties of various hyper-parameter estimators as the sample size goes to infinity.In this paper,we consider one commonly used hyper-parameter estimator,the empirical Bayes(EB).Its convergence in distribution has been studied,and the explicit expression of the covariance matrix of its limiting distribution has been given.However,what we are truly interested in are factors contained in the covariance matrix of the EB hyper-parameter estimator,and then,the convergence of its covariance matrix to that of its limiting distribution is required.In general,the convergence in distribution of a sequence of random variables does not necessarily guarantee the convergence of its covariance matrix.Thus,the derivation of such convergence is a necessary complement to our theoretical analysis about factors that influence the convergence properties of the EB hyper-parameter estimator.In this paper,we consider the regularized finite impulse response(FIR)model estimation with deterministic inputs,and show that the covariance matrix of the EB hyper-parameter estimator converges to that of its limiting distribution.Moreover,we run numerical simulations to demonstrate the efficacy of ourtheoretical results.

Special issue on system identification and estimation

It is our great pleasure and honor to organize this special issue“System Identification and Estimation”.System identification has been a surprisingly lively and resilient area of research in the control community for many years.It grew out of statisticians’interest in time series analysis beginning in the 1940s and became a“regular control topic”in the 1960s,as indicated by thefirst IFAC Symposium on System Identification held in Prague,Czech Republic,in 1967.Sixty years later,it is still an important area of research in thefield of control.It is relevant to ask why the interest in system identification has remained so intense.One answer might be that more and more applications in engineering require mathematical models and the combined use of system identification and physical modeling is the basic way to obtain reliable models.This special issue is focusing on the latest development,trends,and novel methods for system identification and estimation and these contributions will give interesting and inspiring insights into the current status of the area.

Pros and Cons: Autophagy in Acute Spinal Cord Injury

Autophagy plays an important role in the development and pathogenesis of various diseases. It can be induced by a variety of events such as hypoxia, nutrient-starvation, and mechanical damage. Many neurological disorders such Parkinson’s disease, Alzheimer’s disease, amyotrophic lateral sclerosis, Huntington’s disease, cerebral ischemia, and acute spinal cord injury (ASCI), are closely related to autophagy. However, therapeutic strategies to manipulate autophagy have not yet been fully deciphered due to the limited knowledge of the molecular mechanisms underlying autophagy in these disorders.

A survey of neural network accelerators

Machine-learning techniques have recently been proved to be successful in various domains, especially in emerging commercial applications. As a set of machine- learning techniques, artificial neural networks （ANNs）, requiring considerable amount of computation and memory, are one of the most popular algorithms and have been applied in a broad range of applications such as speech recognition, face identification, natural language processing, ect. Conventionally, as a straightforward way, conventional CPUs and GPUs are energy-inefficient due to their excessive effort for flexibility. According to the aforementioned situation, in recent years, many researchers have proposed a number of neural network accelerators to achieve high performance and low power consumption. Thus, the main purpose of this literature is to briefly review recent related works, as well as the DianNao-family accelerators. In summary, this review can serve as a reference for hardware researchers in the area of neural networks.