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Efficient implementation of a neural network-based strategy for the online adaptive control of complex dynamical systems characterized by an interconnection of several subsystems (possibly nonlinear) centers on the rapidity of the convergence of the training scheme used for learning the system dynamics. For illustration, in order to achieve a satisfactory control of a multijointed robotic manipulator during the execution of high speed trajectory tracking tasks, the highly nonlinear and coupled dynamics together with the variations in the parameters necessitate a fast updating of the control actions. For facilitating this requirement, a multilayer neural network structure that includes dynamical nodes in the hidden layer is proposed, and a supervised learning scheme that employs a simple distributed updating rule is used for the online identification and decentralized adaptive control. Important characteristic features of the resulting control scheme are discussed and a quantitative evaluation of its performance in the above illustrative example is given.

Abstract-One key technique for improving the coding efficiency of H.264 video standard is the entropy coder, contextadaptive binary arithmetic coder (CABAC). However the complexity of the encoding process of CABAC is far higher than the table driven entropy encoding schemes such as the Huffman coding. CABAC is also bit serial and its multi-bit parallelization is extremely difficult. For a high definition video encoder, multi-giga hertz RISC processors will be needed to implement the CABAC encoder. In this paper, we provide efficient solutions for the arithmetic coder and the renormalizer. An FPGA implementation of the proposed scheme capable of 54 Mbps encoding rate and test results are presented. A 0.18 µm ASIC synthesis and simulation shows 87 Mbps encoding rate utilizing an area of 0.42 mm 2 .1

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Several novel results concerning the characterization of the equilibrium conditions of a continuous-time dynamical neural network model and a systematic procedure for synthesizing associative memory networks with nonsymmetrical interconnection matrices are presented. The equilibrium characterization focuses on the exponential stability and instability properties of the network equilibria and on equilibrium confinement, viz., ensuring the uniqueness of an equilibrium in a specific region of the state space. While the equilibrium confinement result involves a simple test, the stability results given obtain explicit estimates of the degree of exponential stability and the regions of attraction of the stable equilibrium points. Using these results as valuable guidelines, a systematic synthesis procedure for constructing a dynamical neural network that stores a given set of vectors as the stable equilibrium points is developed.