Browsing by Author "Cipriano, A"

Now showing 1 - 10 of 10
  • Thumbnail Image
    Item
    A new identification method for use in nonlinear prediction
    (IOS PRESS, 2001) Montoya, F; Cipriano, A; Ramos, M
    This paper presents a new identification method for fuzzy models used in nonlinear prediction. The structure and parameters of the fuzzy model are obtained, using input-output data, by minimization of the prediction error. The predictive capacity of the fuzzy model is compared with other linear and non-linear models analyzing an illustrative example. The results show that the new method presents a better behavior.
  • Thumbnail Image
    Item
    A real time visual sensor for supervision of flotation cells
    (PERGAMON-ELSEVIER SCIENCE LTD, 1998) Cipriano, A; Guarini, M; Vidal, R; Soto, A; Sepulveda, C; Mery, D; Briseno, H
    This paper describes an expert system for the supervision of flotation plants based on ACEFLOT, a real time analyzer of the characteristics of the froth that is formed on.:the surface of flotation cells. The ACEFLOT analyzer is based on image processing and measures several physical variables of the froth, including colorimetric, geometric and dynamic information. On the other hand, the expert system detects abnormal operation states and suggests corrective actions, supporting operators on the supervision and control of the flotation plant. (C) 1998 Published by Elsevier Science Ltd. All rights reserved.
  • Thumbnail Image
    Item
    An integrated system for supervision and economic optimal control of mineral processing plants
    (PERGAMON-ELSEVIER SCIENCE LTD, 1999) Munoz, C; Cipriano, A
    This work tackles the problem of dynamically optimising the performance of a mineral concentration plant, taking into account economic profits and technical constraints. The paper proposes a two-level control strategy with regulatory control and the optimisation of an objective function. The regulatory control employs linear model based multivariable predictive controllers with constraints on controlled and manipulated variables, while the optimiser maximises the economic profits using non-linear dynamic models and linear constraints. The results, drawn from simulations, show the proposed strategy to lead to a significant improvement in economic profits when compared against an exclusively regulatory strategy. (C) 1999 Elsevier Science Ltd. All rights reserved.
  • Thumbnail Image
    Item
    Applying approximate linearization to the design of stable fuzzy controllers
    (IOS PRESS, 1999) Concha, J; Cipriano, A
    Fuzzy systems have been successfully applied to the design of knowledge based controllers, yielding very good performance in many cases. However, fuzzy control still lacks general formal analysis and design techniques that allow the designer ensure a priori certain features of the closed-loop system, particularly stability. This article presents a simple systematic design procedure, based on approximate linearization, that guarantees closed-loop asymptotic stability. The method is applicable when a (nonfuzzy) plant model is available and a Takagi-Sugeno fuzzy controller is to be designed.
  • Thumbnail Image
    Item
    Dynamic modelling and advanced multivariable control of conventional flotation circuits
    (PERGAMON-ELSEVIER SCIENCE LTD, 1998) Perez Correa, R; Gonzalez, G; Casali, A; Cipriano, A; Barrera, R; Zavala, E
    Expert and predictive multivariable control algorithms for a conventional cooper flotation circuit were assessed through simulations These simulations were carried out with a nonlinear dynamic model, derived from mass balances and empirical relationships, that qualitatively reproduced the dynamic behaviour of a real plant well. In order to make the simulations more realistic, they included noisy measurements, stochastic parameter variations and input disturbances. New expert algorithms were able to keep the plant operating within a pre-defined zone for long periods without complete control saturation, unlike previous expert controllers. In addition, the inclusion of constraints in a multivariable predictive algorithm verified improved control system regulation and flexibility. (C) 1998 Published by Elsevier Science Ltd. All rights reserved.
  • Thumbnail Image
    Item
    Electromechanical transients simulation on a multicomputer via the VDHN-Maclaurin method
    (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2001) Morales, F; Rudnick, H; Cipriano, A
    This paper reports simulations of power systems electromechanical transients on a multicomputer, formulated as a nonlinear algebraic problem by using the time parallelization concept,:The bi-factorized inversion, which is the most time consuming stage of the simulation, is solved by the "Very Dishonest Newton(VDHN)-Maclaurin" method, a fully parallel indirect method based on the decomposition of the nonupdated Jacobian matrix. This proposal is made to orient the search for the decomposition based on a sufficient condition for the convergence of the Maclaurin series, which is a desirable situation for the design of more robust algorithms for power system simulation. Such condition keeps a close relation with a physical coupling property exhibited by power systems, and the characteristics of the simulation method. Theoretical and numerical results show that a successful implementation of this method can be better reached when the Jacobian matrix is decomposed as a block diagonal matrix plus a matrix with off diagonal blocks elements, the latter representing weak couplings between the diagonal blocks, The epsilon Decomposition is used to satisfy the sufficient condition for convergence and the Longest Path Scheduling Method to prevent the uneven loading of processors, permitting to adapt the method in a efficient way on a coarse grain computer, The parallel simulation was written in C language and implemented on a Parsytec PowerXplorer multicomputer. Test using electromechanical models of the Chilean Central Interconnected system and the IEEE300 test system were made to evaluate the advantages and drawbacks Of the parallel method.
  • Thumbnail Image
    Item
    Estimation of cardiac function from computer analysis of the arterial pressure waveform
    (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 1998) Guarini, M; Urzua, J; Cipriano, A; Gonzalez, W
    This paper presents a method for estimating parameters of a cardiovascular model, including the left-ventricular function, using the sequential quadratic programming (SQP) and the least minimum square (LMS) algorithms. In a first stage, a radial arterial-pressure waveform with corresponding cardiac output are used to automatically seek the set of parameters of the diastolic model. Computer simulation of the model using these parameters generate a pressure waveform and a cardiac output very close to those used for the estimation. In a second stage, the estimated arterial load parameters are used to select the best left-ventricular model function, from four different possibilities, and to estimate its optimum parameter values. The method has been tested numerically and applied to real cases, using data obtained from cardiovascular patients. It has also been subjected to preliminary validation using data obtained from laboratory dogs, in which cardiovascular function was artificially altered.
  • Thumbnail Image
    Item
    Fault detection and isolation using concatenated wavelet transform variances and discriminant analysis
    (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2006) Gonzalez, GD; Paut, R; Cipriano, A; Miranda, DR; Ceballos, GE
    A method for fault detection and isolation is developed using the concatenated variances of the continuous wavelet transform (CWT) of plant outputs. These concatenated variances are projected onto the principal component space corresponding to the covariance matrix of the concatenated variances. Fisher and quadratic discriminant analyses are then performed in this space to classify the concatenated sample CWT variances of outputs in a given time window. The sample variance is a variance estimator obtained by taking the displacement average of the squared wavelet transforms of the current outputs. This method provides an alternative to the multimodel approach used for fault detection and identification, especially when system inputs are unmeasured stochastic processes, as is assumed in the case of the mechanical system example. The performance of the method is assessed using matrices having the percentage of correct condition identification in the diagonal and the percentages misclassified conditions in the off-diagonal elements. Considerable performance improvements may be obtained due to concatenation-when two or more outputs are available-and to discriminant analysis, as compared with other wavelet variance methods.
  • Thumbnail Image
    Item
    Forecasting ozone daily maximum levels at Santiago, Chile
    (PERGAMON-ELSEVIER SCIENCE LTD, 1998) Jorquera, H; Perez, R; Cipriano, A; Espejo, A; Letelier, MV; Acuna, G
    In major urban areas, air pollution impact on health is serious enough to include it in the group of meteorological variables that are forecast daily. This work focusses on the comparison of different forecasting systems for daily maximum ozone levels at Santiago, Chile. The modelling tools used for these systems were linear time series, artificial neural networks and fuzzy models. The structure of the forecasting model was derived from basic principles and it includes a combination of persistence and daily maximum air temperature as input variables. Assessment of the models is based on two indices: their ability to forecast well an episode, and their tendency to forecast an episode that did not occur at the end (a false positive). All the models tried in this work showed good forecasting performance, with 70-95% of successful forecasts at two monitor sites: Downtown (moderate impacts) and Eastern (downwind, highest impacts). The number of false positives was not negligible, but this may be improved by expressing the forecast in broad classes:low, average, high, very high impacts; the fuzzy model was the most reliable forecast, with the lowest number of false positives among the different models evaluated. The quality of the results and the dynamics of ozone formation suggest the use of a forecast to warn people about excessive exposure during episodic days at Santiago. (C) 1998 Elsevier Science Ltd. All rights reserved.
  • No Thumbnail Available
    Item
    One day ahead load forecasting by recurrent neural networks
    (C R L PUBLISHING LTD, 1997) Prina, J; Cipriano, A; Cardenoso, V; Alonso, L; Olmedo, JC; Ramos, M
    In recent years, many applications of neural network methodologies to power system problems have been reported. Among them, short term load forecasting has been one of the most popular. Multilayer perceptron networks have constituted the preferred architecture, achieving successful results. However this network model generally fails to deal with the temporal characteristics of the load signal, being more suitable for static pattern recognition tasks. Dynamic or recurrent networks have shown better capabilities for time signals modeling and forecasting. This paper presents the application of a recurrent network model, which uses a very limited amount of data, to the load forecasting problem. Particularly, the Elman, recurrent model was applied to the 24 hour ahead load forecasting for the Chilean Central Interconnected System (SIC). The load values are considered as a time series, taking advantage of the temporal processing capabilities of this neural network model.