Editorial

Computational intelligence and bioinspired systems

Crystallographic stability is an important factor that affects the stability of perovskites. The stability dictates the commercial applications of lead-based organometal halide perovskites. The tolerance factor ($t$) and octahedral factor ($\mu$) form the state-of-the-art criteria used to evaluate the perovskite crystallographic stability. We studied the crystallographic stabilities of halide and chalcogenide perovskites by exploring an effective alternative descriptor, the global instability index (GII) that was used as an indicator of the stability of perovskite oxides. We particularly focused on determining crystallographic reliability by calculating GII. We analyzed the bond valence models of the 243 halide and chalcogenide perovskites that occupied the lowest-energy cubic-phase structures determined by conducting the first-principles-based total energy minimization calculations. The decomposition energy ($\mathrm{\Delta }{H}_{\mathrm{D}}$) reflects the thermodynamic stability of the system and is considered as the benchmark that helps assess the effectiveness of GII in evaluating the crystallographic stability of the systems under study. The results indicated that the accuracy of predicting thermodynamic stability was significantly higher when GII (73.6%) was analyzed compared to the cases when t (55%) and μ (39.1%) were analyzed to determine the stability. The results obtained from the machine learning-based data mining method further indicate that GII is an important descriptor of the stability of the perovskite family.

This study presents a novel bio-inspired knowledge system, based on closed loop tuning, for calculating the Proportional-Integral-Derivative (PID) controller parameters of a real combined cycle plant. The aim is to automatically achieve the best parameters according to the work point and the dynamics of the plant. To this end, several typical expressions and systems were taken into account to build the model for this multidisciplinary study. Each of these expressions is appropriated for a particular system. The novel method is empirically verified under a real case study based on an auxiliary steam system of a combined cycle plant.

This work aims at a reflection on the evolution of the field of Neurocomputing along the last 20 years that have witnessed the sequence of editions of the International Work-Conference on Artificial Neural Networks (IWANN). This reflection arises inextricably of the evolution of connectionist networks themselves, describing their features and most remarkable particularities, most of which have prevailed in time.

Another trend that is worth mentioning is the development of a strong interconnection with other paradigms comprised under the so-called Computational Intelligence, which can be understood as a set of nature-inspired computational methodologies and approaches to address complex real-world problems, which traditional approaches are ineffective or unfeasible to deal with. Indeed, many hybrid computational intelligence schemes have been developed that efficiently combine procedures from the domains of artificial neural networks, machine learning, evolutionary computation and fuzzy logic to be applied in complex domains.

Finally, a brief description of the diverse contributions that have been included in this special issue is presented. These papers stem from previous versions presented at IWANN2011.