Results regarding H/sup infinity / control for symmetric systems are introduced. It is shown that in order to find the compensator, only one nonlinear matrix equation has to be solved, instead of two Riccati equations as in the general case. A numerically robust algorithm for the solution of the equation introduced is presented. The compensator obtained is also shown to be symmetric. Some considerations concerning the so-called normalized H/sup infinity / control problem are outlined.< >
This paper presents a case study concerning the forecasting of monthly retail time series recorded by the US Census Bureau from 1992 to 2016. The modeling problem is tackled in two steps. First, original time series are de-trended by using a moving windows averaging approach. Subsequently, the residual time series are modeled by Non-linear Auto-Regressive (NAR) models, by using both Neuro-Fuzzy and Feed-Forward Neural Networks approaches. The goodness of the forecasting models, is objectively assessed by calculating the bias, the mae and the rmse errors. Finally, the model skill index is calculated considering the traditional persistent model as reference. Results show that there is a convenience in using the proposed approaches, compared to the reference one.
Kabamba showed the enhanced stability and minimality acquired by using open-loop balanced canonical forms for MIMO system parametrization. This paper shows the fundamental importance of closed-loop balanced representation: under certain conditions stability is guaranteed and minimality assured. For example, one can investigate the stability of closed-loop systems with low order regulator. Sufficient conditions can be derived for the closed-loop dissipativeness and the reduced order compensator stability by using this parametrization, generalizing results proved for SISO systems.
In this paper a clustering analysis based on the combination of the Self-Organizing Map (SOM) and the K-means method is applied to three dimensional ground deformation map obtained by integrating sparse Global Positioning System (GPS) and Differential Interferometric Synthetic Aperture Radar (DInSAR) acquired at Mt Etna in the period 2003-2004. This analysis is aimed to partition the whole displacement field into subsets sharing some common displacements features in order to recognize and classify deformation patterns affecting different sectors of Etna volcano. Results have been also confirmed by a fuzzy c-mean analysis. Introduction The identification of ground deformation movements and the characterization of active faults are priority targets for the geophysical monitoring of Mt Etna, the most active volcano in Europe. In this framework both DInSAR and GPS techniques are successfully used to monitor ground deformation at Mt Etna [1-4]. In order to take advantage of the complementary nature of satellite and geodetic data, current efforts of the scientific community are devoted to develop suitable algorithms able to efficiently integrate these data. Indeed although satellite DInsar enables studying ground deformations with a spatial resolution unprecedented by any other geodetic techniques, it is characterized by a low temporal resolution and provides a mono-dimensional measurement of deformations. On the other hand although GPS is the most suitable technique for measuring ground deformation with sub-cm accuracy level, it provide a point wise 3D displacement vector referring to the specific geodetic benchmark where the antenna is set up; consequently, the spatial resolution of the measurement of the ground deformations is depending from the network geometry and thus is usually low. Here we present an approach to identify deformation patterns based on the joint use of a recently proposed technique to combine DInSAR data and GPS measurements referred to as SISTEM (Guglielmino et al 2009?), and the Self-Organizing Map (SOM). DInSAR and GPS integration (SISTEM method ) Let assume that a geodynamic process (e.g. intrusions of magma or earthquakes) deforms a portion of Earth’s surface; under the hypothesis of infinitesimal and homogeneous strain we define an arbitrary point P, having position x0=(x10, x20, x30), and N surrounding experimental points (EPs) whose positions and displacements are respectively x(n)=(x1(n), x2(n), x3(n)) and u(n)=(u1(n), u2(n), u3(n)) where n=1..N. Is a such hypothesis, adopting a linear approach, the problem of estimating the displacement components Ui (i=1..3) of the point P, from the experimental data u(n)=(u1(n), u2(n), u3(n)), can be modelled by the N equations [6]: ) 3 .. 1 , ( ) ( ) ( ) ( = + ∆ = j i U x H x u i n j ij n i (1) where ∆xj(n)=xj(n)-xj0 are the relative positions of the n th EP experimental points and the arbitrary point P and
This paper deals with the problem of automatic detection of offsets in GPS time series, which is of interest both in active volcanic and tectonic areas, where they often signal either the opening of eruptive fissures or seismic and aseismic dislocations. The problem is tackled by using the Change Point Detection (CPD) approach. Results show that CPD algorithms are suitable both in off-line and on-line frameworks. In particular, we show that CPD algorithms could contribute to the implementation of a warning system of volcanic intrusive activity.
In this paper the problem of controlling the temperature on an RTP system is addressed. The key points of the contribution are the following: a neural model of the considered system is obtained. Based on this model an optimal controller is derived. The goodness of the control system behaviour are shown also in comparison with those reported in literature.
Paroxysmal explosive activity at Etna volcano (Italy) became very common during the last three decades, rising concern on the civil protection authorities because of its tremendous impact on the local population, infrastructures, viability and air traffic. Between 4 July and 15 August 2024, during the peak of the touristic season when the local population doubles, Etna volcano gave rise to a sequence of six paroxysmal explosive events from the summit crater named Voragine. This is the oldest and largest of the four Etna’s summit craters and normally displays just degassing, with the previous explosive sequences occurred in December 2015 and May 2016. In this paper, we use thermal images recorded by the monitoring system maintained by the Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Etneo (INGV–OE) and an automatic routine previously tested in order to automatically define the starting and ending time of each episode, the duration, total volume of erupted fluids (gas plus pyroclastic material), erupted volume of pyroclastic material, time-averaged discharge rate (TADR), and height (maximum and average) of the lava fountains. These data allowed us to infer the eruptive processes and gain some insights on the evolution of the explosive sequences useful for hazard assessment.
