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To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.compeleceng.2009.02.008 Byline: Xianghui Cao (a), Jiming Chen (a), Chuanhou Gao (b), Youxian Sun (a) Keywords: Wireless sensor and actuator networks; Optimal control; Estimation; Packet loss Abstract: The wireless sensor/actuator networks (WSANs) can be used for spatially distributed control systems. With smart sensors and actuators, the WSANs are able to not only sense the control system states and report measurements, but also perform control and actuation. This paper investigates WSANs on their ability of control. A centralized controller is introduced into WSANs to make up closed-loop control systems, in which control decisions are made based on global network-wide information. A model of the control and communication over WSANs is made theoretically, based on which we achieved an optimal control method. It is demonstrated by simulations that the control method proposed could stabilize the control system quickly. Author Affiliation: (a) State Key Lab. of Industrial Control Technology, Institute of Industrial Process Control, Zhejiang University, Hangzhou 310027, PR China (b) Institute of Systems Optimization Techniques, Zhejiang University, Hangzhou 310027, PR China Article History: Received 7 March 2008; Accepted 16 February 2009

In this paper, the recurrent neural networks (RNNs) with a generalized activation function class is proposed. In this proposed model, every component of the neuron's activation function belongs to a convex hull which is bounded by two odd symmetric piecewise linear functions that are convex or concave over the real space. All of the convex hulls are composed of generalized activation function classes. The novel activation function class is not only with a more flexible and more specific description of the activation functions than other function classes but it also generalizes some traditional activation function classes. The absolute exponential stability (AEST) of the RNN with a generalized activation function class is studied through three steps. The first step is to demonstrate the global exponential stability (GES) of the equilibrium point of original RNN with a generalized activation function being equivalent to that of RNN under all vertex functions of convex hull. The second step transforms the RNN under every vertex activation function into neural networks under an array of saturated linear activation functions. Because the GES of the equilibrium point of three systems are equivalent, the next stability analysis focuses on the GES of the equilibrium point of RNN system under an array of saturated linear activation functions. The last step is to study both the existence of equilibrium point and the GES of the RNN under saturated linear activation functions using the theory of M-matrix. In the end, a two-neuron RNN with a generalized activation function is constructed to show the effectiveness of our results. Index Terms--Absolute exponential stability (AEST), convex hull, generalized activation function class, piecewise linear function, recurrent neural networks (RNNs).

The strong [[gamma].sub.[kappa]]-[[gamma].sub.cl][H.sub.[infinity]] stabilization problem is addressed in this paper. Based on the bounded real lemma, an improved sufficient condition on strong [H.sub.[infinity]] stabilization as well as its dual form is derived, which extends the ARE constraint in the former results into an inequality constraint. Two path-following algorithms are proposed to solve the associated optimization problems with BMI constraints, which lead to full order controllers. A numerical example is presented to demonstrate the effectiveness of the result. Index Terms--Bilinear matrix inequality (BMI), [H.sub.[infinity]] control, linear matrix inequality (LMI), strong stabilization.