Far from equilibrium computation and particle swarm optimization.
Featureless pattern classification
In this paper the possibilities are discussed for training statistical pattern recognizers based on a distance representation of the objects instead of a feature representation. Distances or similarities are used between the unknown objects to be classified with a selected subset of the training objects (the support objects). These distances are combined into linear or nonlinear classifiers. In this approach the feature definition problem is replaced by finding good similarity measures. The proposal...
Free-form surfaces for scattered data by neural networks.
From elementary martingale calculus to rigorous properties of mixtures of experts.
Function approximation of Seidel aberrations by a neural network
This paper deals with the possibility of using a feedforward neural network to test the discrepancies between a real astronomical image and a predefined template. This task can be accomplished thanks to the capability of neural networks to solve a nonlinear approximation problem, i.e. to construct an hypersurface that approximates a given set of scattered data couples. Images are encoded associating each of them with some conveniently chosen statistical moments, evaluated along the axes; in this...
Fuzzy decision trees to help flexible querying
Fuzzy data mining by means of the fuzzy decision tree method enables the construction of a set of fuzzy rules. Such a rule set can be associated with a database as a knowledge base that can be used to help answering frequent queries. In this paper, a study is done that enables us to show that classification by means of a fuzzy decision tree is equivalent to the generalized modus ponens. Moreover, it is shown that the decision taken by means of a fuzzy decision tree is more stable when observation...
Fuzzy grammatical inference using neural network.
We have shown a model of fuzzy neural network that is able to infer the relations associated to the transitions of a fuzzy automaton from a fuzzy examples set. Neural network is trained by a backpropagation of error based in a smooth derivative [1]. Once network has been trained the fuzzy relations associated to the transitions of the automaton are found encoded in the weights.
Fuzzy neural network approach to fuzzy polynomials.
In this paper, an architecture of fuzzy neural networks is proposed to find a real root of a dual fuzzy polynomial (if exists) by introducing a learning algorithm. We proposed a learning algorithm from the cost function for adjusting of crisp weights. According to fuzzy arithmetic, dual fuzzy polynomials can not be replaced by a fuzzy polynomials, directly. Finally, we illustrate our approach by numerical examples.
Fuzzy Sequential Pattern Mining in Incomplete Databases.
Fuzzy systems and neural networks XML schemas for Soft Computing.
This article presents an XML[2] based language for the specification of objects in the Soft Computing area. The design promotes reuse and takes a compositional approach in which more complex constructs are built from simpler ones; it is also independent of implementation details as the definition of the language only states the expected behaviour of every possible implementation. Here the basic structures for the specification of concepts in the Fuzzy Logic area are described and a simple construct...