IDENTIFICATION OF NONLINEAR VEHICLE DYNAMICS WITH UNOBSERVABLE INPUT

The realization problem and identification procedure of simple nonlinear vehicle dynamics are studied using the estimated spectrum and bispectrum of the output (vertical acceleration) process when the input excitation is (in real time) unobservable

NONPARAMETRIC IDENTIFICATION OF NONLINEAR ZADEH MODELS USING GAUSSIAN AUTOREGRESSIVE INPUT PROCESSES

The paper presents a nonparametric identification method for the determination of the kernels of nonlinear analytic Zadeh models if the input signal is a Gaussian stationary autoregressive process

Identification of Supply Chains Based on Input-Output Data

The paper focuses on supply chain modeling issues, namely how subspace identification techniques can be used to characterize the strength of relations between certain system parameters. This might be useful when no knowledge about the internal workings or inner structure of the system is available, thus only blackbox like approaches can be utilized. Here let us show how supply chains can be identified and modeled by deterministic linear state space models and how the accuracy of the identified model reflects the relation between certain system parameters

AN ENERGY STRATEGY FOR PUBLIC TRANSPORT SYSTEMS

The paper describes the energy consumption processes in a public transport system and identifies areas where savings can be made. An assessment is undertaken of the cost of achieving energy savings, the effectiveness of those savings and a priority proposed for realising them. The paper also discusses the role of different fuels and the trends in future availability. From this proposals are made for changing fuel sourcesm, to make public transport less vulnerable to market price fluctuations

Tradeoff between Approximation Accuracy and Complexity: HOSVD Based Complexity Reduction

Higher Order Singular Value Decomposition (HOSVD) based complexity reduction method is proposed in this paper to polytopic model approximation techniques. The main motivation is that the polytopic model has exponentially growing computational complexity with the improvement of its approximation property through, as usually practiced, increasing the density of local linear models. The reduction technique proposed here is capable of defining the contribution of each local linear model, which serves to remove the weakly contributing ones according to a given threshold. Reducing the number of local models leads directly to the complexity reduction. The proposed reduction can also be performed on TS fuzzy model approximation method. A detailed illustrative example of a non-linear dynamic model is also discussed. The main contribution of this paper is the multi-dimensional extension of the SVD reduction technique introduced in the preliminary work [1]. The advantage of this extension is that the HOSVD based technique of this paper can be applied to polytopic models varying in a multi-dimensional parameter space unlike the reduction method of [1] which is designed for one dimensional parameter space