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This paper addresses the important issues of error control for video transmission over 3G, considering the fact that wireless video delivery faces the huge challenge of the high error rate and time variability in wireless channel. This paper proposes a real world statistics based event-trigger bit error rate (BER) model, which can describe and handle the time-varying wireless channel error characteristics better. Moreover, a recurrent neural network is employed to decide the state transfer as a mechanism. Simulation results and comparisons demonstrate effectiveness and efficiency of the proposed method in term of visual performance and transmission efficiency over a variety of wireless channel conditions. [ABSTRACT FROM AUTHOR]

Evolving artificial neural network is an important issue in both evolutionary computation (EC) and neural networks (NN) fields. In this paper, a hybrid particle swarm optimization (PSO) is proposed by incorporating differential evolution (DE) and chaos into the classic PSO. By combining DE operation with PSO, the exploration and exploitation abilities can be well balanced, and the diversity of swarms can be reasonably maintained. Moreover, by hybridizing chaotic local search (CLS), DE operator and PSO operator, searching behavior can be enriched and the ability to avoid being trapped in local optima can be well enhanced. Then, the proposed hybrid PSO (named CPSODE) is applied to design multi-layer feed-forward neural network. Simulation results and comparisons demonstrate the effectiveness and efficiency of the proposed hybrid PSO. [ABSTRACT FROM AUTHOR]

A nonlinear adaptive predictive control strategy using orthogonal wavelet network model is presented. Based on a set of orthogonal wavelet functions, wavelet neural network performs a nonlinear mapping from the network input space to the wavelons output space in hidden layer. Its weight coefficients can be simply estimated by a linear least-square estimation algorithm. The excellent statistic properties of the weight parameters of wavelet network also can be obtained. A single input single output (SISO) nonlinear predictive control strategy is implemented in the simulation of a Polypropylene process. [ABSTRACT FROM AUTHOR]

On-line Radial Basis Function (RBF) neural network based multiple steady states controller for nonlinear system is presented. The unsafe upper steady states can be prevented with the optimizer for Constrained General Model Controller (CGMC).Process simulator package is used to generate a wide range of operation data and the dynamic simulator is built as the real plant. The effectiveness is illustrated with a Continuous Stirred Tank Reactor (CSTR) and OPC tools are developed for on-line data acquisition and computation. [ABSTRACT FROM AUTHOR]