Catálogo de publicaciones - libros

We use techniques from Kleinberg’s Hubs and Authorities and kernel functions as in Support Vector Machines to define a new form of clustering. The increase in the degree of non linearity of the kernels leads to an increase in the granularity of the data space and to a natural evolution of clusters into subclusters. The algorithm proposed to construct zoomed clusters has been designed to run on very large data sets as found in web directories and bioinformatics.

This paper discusses the use of a recent boosting algorithm for recurrent neural networks as a tool to model nonlinear dynamical systems. It combines a large number of RNNs, each of which is generated by training on a different set of examples. This algorithm is based on the boosting algorithm where difficult examples are concentrated on during the learning process. However, unlike the original algorithm, all examples available are taken into account. The ability of the method to internally encode useful information on the underlying process is illustrated by several experiments on well known chaotic processes. Our model is able to find an appropriate internal representation of the underlying process from the observation of a subset of the states variables. We obtain improved prediction performances.

This paper presents an approach to the prediction of mineral prospectivity that provides an assessment of uncertainty. Two feed-forward backpropagation neural networks are used for the prediction. One network is used to predict degrees of favourability for deposit and another one is used to predict degrees of likelihood for barren, which is opposite to deposit. These two types of values are represented in the form of truth-membership and false-membership, respectively. Uncertainties of type error in the prediction of these two memberships are estimated using multidimensional interpolation. These two memberships and their uncertainties are combined to predict mineral deposit locations. The degree of uncertainty of type vagueness for each cell location is estimated and represented in the form of indeterminacy-membership value. The three memberships are then constituted into an interval neutrosophic set. Our approach improves classification performance compared to an existing technique applied only to the truth-membership value.

Thermal deformation is a nonlinear dynamic phenomenon and is one of the significant factors for the accuracy of machine tools. In this study, a dynamic feed-forward neural network model is built to predict the thermal deformation of machine tool. The temperatures and thermal deformations data at present and past sampling time interval are used train the proposed neural model. Thus, it can model dynamic and the nonlinear relationship between input and output data pairs. According to the comparison results, the proposed neural model can obtain better predictive accuracy than that of some other neural model.

One of the frequently used forecasting methods is the time series analysis. Time series analysis is based on the idea that past data can be used to predict the future data. Past data may contain imprecise and incomplete information coming from rapidly changing environment. Also the decisions made by the experts are subjective and rest on their individual competence. Therefore, it is more appropriate for the data to be presented by fuzzy numbers instead of crisp numbers. A weakness of traditional crisp time series forecasting methods is that they process only measurement based numerical information and cannot deal with the perception-based historical data represented by fuzzy numbers. Application of a fuzzy time series whose values are linguistic values, can overcome the mentioned weakness of traditional forecasting methods. In this paper we propose a fuzzy recurrent neural network (FRNN) based fuzzy time series forecasting method using genetic algorithm. The effectiveness of the proposed fuzzy time series forecasting method is tested on benchmark examples.

As the development of the electrical power market, the maintenance automation has become an intrinsic need to increase the overall economic efficiency of hydropower plants. A Multi-Agent System (MAS) based model for the predictive maintenance system of hydropower plant within the framework of Intelligent Control-Maintenance-Management System (ICMMS) is proposed. All maintenance activities, form data collection through the recommendation of specific maintenance actions, are integrated into the system. In this model, the predictive maintenance system composed of four layers: Signal Collection, Data Processing, Diagnosis and Prognosis, and Maintenance Decision-Making. Using this model a prototype of predictive maintenance for hydropower plant is established. Artificial Neural-Network (NN) is successfully applied to monitor, identify and diagnosis the dynamic performance of the prototype system online.

As long leading-time hydrological forecast is a complex non-linear procedure, traditional methods are easy to get slow convergence and low efficiency. The basic relevance vector machine (BRVM) and the developed sequential relevance vector machine (SRVM) are employed to forecast multi-step ahead hydrological time series. The relevance vector machine is a sparse approximate Bayesian kernel method, and it provides full probabilistic forecasting results, which is helpful for hydrological engineering decision. BRVM and SRVM are respectively applied to the annual coming runoff forecast of Three Gorges hydropower station as case study. When compared with auto regression moving average models, BRVM exhibits high model efficiency and provides satisfying forecasting precision. SRVM is potential for its increased freedom and adaptive model selection mechanism. Comparison is also made within direct forecast and iterative one-step ahead forecasting for multi-step ahead forecasting, and the latter shows the ability of highlighting the model performance.

How to exploit current information techniques for rapidly and accurately building a fittest neural network becomes increasingly significant for flood peak forecasting. This paper firstly designs a distributed computing architecture and builds a computing environment based on Grid technologies. Then a distributed computing service for neural networks based on a genetic algorithm and a modified BP algorithm is designed and developed to rapidly and accurately building a fittest neural network for flood peak forecasting. Finally, a distributed computing prototype system is developed and implemented on a case study of the flood prevention in Shenzhen city, China. The experiment result shows that the scheme addressed in the paper is efficient and feasible.

In this paper, we demonstrate that cabinet approval ratings can automatically be inferred with good performance by a neural network technique, that is, information-theoretic competitive learning. Because cabinet approval rating estimation is an extremely complex process with much non-linearity, neural networks may give much better performance than conventional statistical methods. Though an attempt to infer public opinions seem to be a challenging topic for machine learning, little attempts have been made to infer approval ratings to our best knowledge. In this context, we try to apply information-theoretic competitive learning to the problem of cabinet approval ratings. Information-theoretic competitive learning has been developed so as to simulate competitive processes of neurons. One of the main characteristics of the method is that it is a very soft-type of competitive learning in which conventional competitive learning is only a special case. Though the method seems to be promising due to its general property, we have had a few experimental results to show better performance. Experimental results show that without any teacher information neural networks can appropriately infer the rise and fall of approval ratings through a process of information maximization. This experiment result surely opens up new perspectives for neural networks as well as mass communication studies.

Radial Basis Function Neural Network are known in scientific literature for their abilities in function approximation. Above all, this particular kind of Artificial Neural Network is applied to time series forecasting in non-linear problems, where estimation of future samples starting from already detected quantities is very hardly. In this paper Radial Basis Function Neural Network was implemented in order to predict the trend of () for aftershocks temporal series, that is the numerical series of daily-earthquake’s number occurred after a great earthquake with magnitude > 7.0 Richter. In particular we implemented the RBF-NN for the Colfiorito seismic sequence. The seismic sequences considered in this work are obtained following criteria already known in scientific literature [1], [2]. Results of proposed approach are very encouraging.