Catálogo de publicaciones - libros

A hybrid network, based on the integration of Fuzzy ARTMAP (FAM) and the Rectangular Basis Function Network (RecBFN), is proposed for rule learning and extraction problems. The underlying idea for such integration is that FAM operates as a classifier to cluster data samples based on similarity, while the RecBFN acts as a “compressor” to extra and refine knowledge learned by the trained FAM network. The hybrid network is capable of classifying data samples incrementally as well as of acquiring rules directly from data samples for explaining its predictions. To evaluate the effectiveness of the hybrid network, it is applied to a fault detection and diagnosis task by using a set of real sensor data collected from a Circulating Water (CW) system in a power generation plant. The rules extracted from the network are analyzed and discussed, and are found to be in agreement with experts’ opinions used in maintaining the CW system.

Model based predictive control has become an important form of advanced control. Generalised predictive controllers () have been successfully applied to control complex processes in the industry during the last decade. In this paper, we present a novel method named modified particle swarm optimization, which is based on Gaussian and Cauchy distributions for the optimization of an adaptive using an (Auto Regressive Moving Average) mathematical model. The simulation results demonstrated the successful tuning and the suitability of the modified particle swarm optimization for the control of a non-linear process of Hammerstein type.

IQBE has been shown as a promising technique to assist the users in the query formulation process. In this framework, queries are automatically derived from sets of documents provided by them. However, the different proposals found in the specialized literature are usually validated in non realistic information retrieval environments. In this work, we design several experimental setups to create real-like retrieval environments and validate the applicability of a previously proposed multiobjective evolutionary IQBE technique for fuzzy queries on them.

In practical system identification it is often desirable to simultaneously handle several objectives and constraints. In some cases, these objectives and constraints are often non-commensurable and the objective functions are explicitly/mathematically not available. In this paper, Interactive Evolutionary Computation (IEC) is used to effectively handle these identification problems. IEC is an optimization method that adopts evolutionary computation (EC) among system optimization based on subjective human evaluation. The proposed approach has been implemented in MATLAB (EAsy-IEC Toolbox) and applied to the identification of a pilot batch reactor. The results show that IEC is an efficient and comfortable method to incorporate a priori knowledge of the user into a user-guided optimization and identification problems. The developed EASy-IEC Toolbox can be downloaded from the website of the authors: http://www.fmt.vein.hu/softcomp/EAsy.

Genetic Algorithm (GA) is generating considerable interest for solving industrial optimisation problems. It is proving robust in delivering global optimal solutions and helping to resolve limitations encountered in traditional methods. However there are fewer GA applications in the process optimisation. This paper presents an overview of recent GA applications in process optimisation. The paper explores the features of process optimisation and critically evaluates how current GA techniques are suited for such complex problems. The survey outlines the current status and trends of GA applications in process related industries. For each industry, the paper describes the general domain problem, common issues, current trends, and the improvements generated by adopting the GA strategy. The paper concludes with an outline of future research directions.