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This paper first reviews wavelet transforms and multiresolution analysis. Then, the structures and learning algorithms of orthogonal wavelet networks(OWN) are discussed. The orthogonal wavelets are used as activation functions of the hide nodes. Finally, nonlinear dynamic system identification using the orthogonal wavelet networks is investigated. Computer simulations verify the theoretical result.

The problem of Hinfin analysis and synthesis of linear uncertain systems is addressed via parameter-dependent Lyapunov functions. The polyqudratic Hinfin stability as well as the affine quadratic Hinfin stability is defined, and the corresponding sufficient conditions of LMI form are derived based on a descriptor system approach. By refraining the slack matrices, Hinfin state feedback controllers could also be constructed by LMI constraints. The effectiveness is demonstrated by a numerical example.

For the strong gammak-gammacl Hinfin stabilization problem, a new bounded real lemma is provided by adding two slack variables, which provides extra freedom to include more control constraints in handling robust control problems. With the extended bounded real lemma, a new sufficient condition is proposed for n-th order strong gammak-gammacl Hinfin stabilization. Furthermore, the Hinfin stable stabilization and strong stabilization problems can also be solved by relaxing certain constraints accordingly. Despite the BMI constraints, the path-following and alternative iteration methods are adopted for the controller design. The effectiveness of the algorithms is indicated by a numerical example.

Orthogonal polynomials and functions have been used extensively by many researchers to solve control problems such as those of optimal control, system analysis and identification. In this paper, a general orthogonal polynomial operator (GOPO) is proposed and its properties are studied. The new operator is linear, bounded and continuous from the Hilbert space L2ρ[O,T] to a Banach space Mm. A class of three-dimensional matrix is introduced and a set of operational matrixes are summarized to establish operational rules of GOPO. Ten rules similar to those of Laplace operator have been set up. by which a lot of problems in control field can be approximately solved under rigorous mean-square covergency. The new GOPO approach is not only more rigorous and more general than those existing GOPs methods, but also straightforward and convenient for computation. Its applications to systems analysis and identification of time-varying systems are discussed and illustrative examples are given to show the effectiveness of the new GOPO method.

Many centralized fusion estimation algorithms assumed that measurement noises among sensors are uncorrelated, but it is not true when there exist the same unmodeled measurement noise source. In this paper, based on the matrix similarity transform, covariance matrix of correlated measurement noise is successfully parallel decomposed and the linear measurement models are transformed. Optimal centralized fusion estimation algorithm is presented. When measurement noises are uncorrelated, the results here are reduced to the standard optimal centralized fusion estimation algorithms.

Gives a comprehensive analysis on the sampled-data H/sub 2/ optimal control problem. An approach based on Riccati equations with jumps is presented. Compared to the lifting-based method, this approach is more direct and explicit. Then we show that the two methods lead to identical synthesis equations for the H/sub 2/ optimal sampled-data controller.

Discusses approximate modelling and adaptive control of discrete time linear time-varying systems (LTVS). A novel model, the representation of LTVS in the wavelet time-frequency space, is established based on discrete wavelet transforms. The joint time-frequency properties of wavelet analysis are appropriate for describing the nonstationary characteristic of LTVS. In this model parameter estimation is fulfilled by means of some algebraic calculations. Finally, an adaptive predictive control scheme is proposed based on this representation. Simulation results demonstrate its good behaviour.

The paper is concerned with the design of robust guaranteed cost controller with H/sub /spl infin//-/spl gamma/ disturbance attenuation performance for linear systems with norm bounded parameter uncertainties and disturbances.

Guaranteed cost control (GCC) via output feedback for sampled data systems is considered. In terms of a Riccati inequality with jumps, the GCC problem for a sampled data system is defined. For uncertain sampled data systems, the GCC controller can stabilize the closed loop system and guarantee an adequate level of performance. Motivated by some results on H/sub /spl infin// control for sampled data systems, we transform the GCC problem to a H/sub /spl infin// control for a linear system with jumps. Simulation results show the GCC is efficient and practicable.