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Two methods have been developed for real time identification and classification of the roughness pattern of road surfaces using the neural network. These methods are directly available both for semi-active and active vibration controls of cars. Accelerations of the rear wheel axis under the suspension are used as the input data for real time identification. The neural network which has acquired the informations of the seven typical roughness patterns is used for real time classification of actual road surfaces during driving. Validity and usefulness of these methods are verified by simulation.

In this paper, a new method which directly identifies characteristic matrices (the mass, damping and stiffness matrices) of the mechanical structure using measured forces input and responses data is proposed. This algorithm is based upon the Maximum Likelihood Estimation, so that the accuracy of identified matrices is stable to experimental errors (random errors). After a theoretical formulation is performed, two examples are provided to illustrate and validate this algorithm. One is analytical example which identifies analytically generated data with random noises, and the other experimentarly identified engine/mount system of automobiles.

This paper concerns the Direct System Identification Method (hereafter referred to as DSIM) which allows accurate and quick determination of two groups of properties which exercise dominant effects on low frequency vibration of a vehicle body. The first group is the rigid body properties of an engine. The second group is the properties of each engine mount. Under the assumption that the engine/mount system is a rigid body, this paper makes theoretical discussion for using the DSIM to induce the parameters of an engine/mount system, and makes improvements for better correlation with experiments. Also mentioned is a comparison of this study with the experimental results and verification of consistency on those parameters obtained from DSIM to predict the accurate vehicle characteristics, along with the role this method will play in upgrading the technology of prediction analysis.