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This paper presents an application of wavelet networks in identification and control design for a class of non-linear dynamical systems. The technique of feedback linearization, supervisory control and H∞control are used to design an adaptive control law and also the parameters of the wavelet networks are adapted using a Lyapunov-based design. By some theorems, it will be proved that even in the presence of modelling errors, named network error, the stability of the overall identification scheme and the convergence of the network parameters and the boundedness of the state errors are guaranteed.

The problem of Lyapunov Exponents (LEs) estimation from chaotic data based on Jacobian approach by polynomial models is considered. The optimum embedding dimension of reconstructed attractor is interpreted as suitable order of model. Therefore, based on global polynomial mode ling of system, a novel criterion for selecting the embedding dimension is presented. By considering this dimension as the model order, the best nonlinearity degree of polynomial is estimated. The selected structure is used for local estimating of Jacobians to calculate the LEs. This suitable structure of polynomial model leads to better results and avoids of sporious LEs. Simulation results show the effectiveness of proposed methodology.

This paper analyzes the modeling process by means of a field study in the chemical industries. A diversified set of experienced modelers from two different countries have been interviewed using a case study approach. The interviews focussed on the modeling process. Other issues like life-cycle perspectives and the use of modeling in the process industries were treated as well. The information has been summarized and presented in a structured manner imposing the least possible bias from the authors. Based on the interview information the modeling process is discussed in detail. Further, fundamental research issues are identified, and a research agenda is proposed.

The decoupling of the input - output behaviour of linear multivariable systems by constantstate- feedback generally requires the compensation of all invariant zeros, which causes unstability in the case of non - minimum phase systems. The paper presents a method for partial but stable decoupling, ensuring without compensations coupling of only oneoutput with several inputs. The application of a dynamicstate-feedback however allows complete and stable decoupling with the disadvantage of an increased number of disagreeable transmission zeros.