A fast method for fuzzy neural modeling and refinement

In the identification of complex dynamic systems using fuzzy neural networks, one of the main issues is the curse of dimensionality, which makes it difficult to retain a large number of system inputs or to consider a large number of fuzzy sets. Moreover, due to the correlations, not all possible network inputs or regression vectors in the network are necessary and adding them simply increases the model complexity and deteriorates the network generalisation performance. In this paper, the problem is solved by first proposing a fast algorithm for selection of network terms, and then introducing a refinement procedure to tackle the correlation issue. Simulation results show the efficacy of the method.

La lógica poética. Una fenomenología del pensamiento artístico

We tried to extend the notion of truth, from the rational concept towards the subjective one. We not only exist by the thought, but also by the feeling. If we apply the scientific method to everything, we remain without art. We want to adhere ourselves to the intimate experience of the subjective thing. In art, the eye introduces the perspective and eliminates the interpretation possibility. Something similar happens with the matter: verb is to matter as name is to form. The Academy has given importance to the form, and it has forgotten the poetical qualities of the matter; it has focused on the figure and it has forgotten the background. However, we have to pay attention to the accidental thing, to the rupture of the protocol, to the frenzy of everything that is alive. For this reason, we pay attention to our intuition, to the sleepy subsoil of our ordinary experience whose inertia has lost the disruptive value of the action. At the end of this article we offer practical exercises, with which we wish to revitalize our centres of attention.

Dynamic pricing model and algorithm for perishable products with fuzzy demand (ABS 2* Journal)

This paper studies the dynamic pricing problem of selling fixed stock of perishable items over a finite horizon, where the decision maker does not have the necessary historic data to estimate the distribution of uncertain demand, but has imprecise information about the quantity demand. We model this uncertainty using fuzzy variables. The dynamic pricing problem based on credibility theory is formulated using three fuzzy programming models, viz.: the fuzzy expected revenue maximization model, a-optimistic revenue maximization model, and credibility maximization model. Fuzzy simulations for functions with fuzzy parameters are given and embedded into a genetic algorithm to design a hybrid intelligent algorithm to solve these three models. Finally, a real-world example is presented to highlight the effectiveness of the developed model and algorithm.