Conditional belief networks introduce stochastic binary variables in neural networks. Contrary to a classical neural network, a belief network can predict more than the expected value of the output Y given the inputX . It can predict a distribution of outputs Y which is useful when an input can admit multiple outputs whose average is not necessarily a valid answer. Such networks are particularl...

In this paper, we present a novel way to summarize the structure of large graphs, based on non-parametric estimation of edge density in directed multigraphs. Following coclustering approach, we use a clustering of the vertices, with a piecewise constant estimation of the density of the edges across the clusters, and address the problem of automatically and reliably inferring the number of clust...

For standard fuzzy systems where the input membership functions are defined on a grid on the input space, and all possible combinations of rules are used, there is an exponential growth in the number of parameters of the fuzzy system as the number of input dimensions increases. This “curse of dimensionality” effect leads to problems with design of fuzzy controllers (e.g., how to tune all these ...

Learning from data streams is a contemporary and challenging issue due to the increasing rate of the size and temporal availability of data, turning traditional learning methods impracticable. This work addresses a structure and introduces a learning approach to train uninorm-based hybrid neural networks using extreme learning concepts. Uninorms bring flexibility and generality to fuzzy neuron ...

The torque in single-spun yarns is an inherent property of the twisting and bending of staple fibres during the formation of yarn combined with the effect of applied tension on the yarn. The consequences of yarn torque are well known and are widely observed as yarn instability, e.g., yarn rotation under tension; local snarling and entanglement at low loads, and as distortion in fabric, i.e., ed...

A general approach for system identification with artificial neural networks is presented. A dynamic process can be represented as a function of the evolution of the inputs/outputs of the system. The standard approach to system identification is to describe the system by a set of equations that take into account the underlying mechanisms of the studied plant. If the equations of the system are ...

Many studies on modeling of fuzzy inference systems have been made. The issue of these studies is to construct automatically fuzzy systems with interpretability and accuracy from learning data based on meta-heuristic methods. Since accuracy and interpretability are contradicting issues, there are some disadvantages for self-tuning method. Obvious drawbacks of the method are lack of interpretabi...