The new index LAeg of the environmental noise has the problem that the measured value is strongly influenced by the temporal and/or unexpected sounds of the high noise level and fluctuates unstably. This paper focuses on the road traffic noise as an example of the noise and proposes a method for detecting such non-existent sounds from the great amount of measured data. Concretely, random variables characterizing the object of study are introduced and their distribution function is derived from empirical knowledge. After confirming the validity of this study by the simulation experiment, the function is applied to the actually measured data in order to investigate the availability of statistically detecting the abnormal sounds.
This paper extends the off-line reference shaping method proposed by the authors to the case where non-parametric uncertainties exist in closed loop systems. The method guarantees that input/state constraints are satisfied in the presence of uncertainty, while it improves the tracking performance. More importantly, the effectiveness of the proposed method is evaluated by simulation and experiment. In particular, it demonstrates the difference between the nominal reference shaping and the proposed robust one.
The TAM (Topographic Attentive Mapping) Network based on a biologically-motivated neural network model is an especially effective model. When the network makes an incorrect output prediction, the attentional feedback circuit modulates the learning rates and adds a node to the category layer in order to improve the network's prediction accuracy. In this paper, a pruning algorithm for reducing links and nodes at the layers is proposed. The usefulness of the algorithm is also illustrated.
In this paper, we consider a subspace identification method for linear stochastic systems subject to observation outliers, where the observation noise contains large values with a low probability. We derive a subspace identification method by combining the orthogonal decomposition-based subspace identification method (ORT-method) and a weighted LQ decomposition. We apply the ORT-method to the input-output data, coupled with the standard LQ decomposition to obtain residuals of the output sequence. By using the median of residuals, outliers are detected by a simple scheme in robust statistics. Based on detected outliers, a weighting matrix is generated automatically, and is incorporated in the weighted LQ decomposition to get an improved estimate of the system matrices. A numerical example is included to show the effectiveness of the proposed method.
An alternative method is proposed to construct a guaranteed cost control law for neutral delay-differential systems with uncertain parameters. The feature of this method is that it is fit for the state space not W12 but M2, in the points : i) the form of the feedback law, ii) the form of the cost functional, and iii) the form of Liapunov function. A sufficient condition to obtain the feedback law is given, and its necessary and sufficient condition is introduced with linear matrix inequalities (LMIs). The feedback gain can be calculated with a solution of the LMIs. Furthermore, the upper bound of the cost functional can be minimized by a convex optimization. A numerical example is given to demonstrate the design procedure.