Solid lubricated bearings are common components in space mechanisms, and their reliability and performance degradation assessment are very crucial. In this study, a fuzzy self-organizing map method is used to perform performance degradation assessment. Feature vectors are constructed by indices of vibration as well as friction torque signal. Self-organizing map is then used to perform performan...

The aim of this paper is to provide a sound framework for addressing a difficult problem: the automatic construction of an autonomous agent’s modular architecture. We briefly present two apparently uncorrelated frameworks: Autonomous planning through Markov Decision Processes and Kernel Clustering. Our fundamental idea is that the former addresses autonomy whereas the latter allows to tackle se...

How can we have confidence that self organizing systems actually do what we expect them to? In this position paper we overview some mechanisms at the basis of controlling and predicting the behavior of autonomous and self-organizing systems despite components’ autonomy and non-deterministic behavior. In particular we focus the analysis on the SAPERE ecosystem as an exemplary model to frame the ...

The method of self-organizing maps (SOM) is a method of exploratory data analysis used for clustering and projecting multi-dimensional data into a lower-dimensional space to reveal hidden structure of the data. The Self-Organizing Feature Maps (SOFMs) [11] is a class of neural networks capable of recognizing the main features of the data they are trained on. There is extensive literature on its...

This paper proposes an on-line self-organizing fuzzy logic controller (FLC) design applied to the control of vibrations in flexible structures containing distributed piezoelectric actuator patches. In this methodology, the fuzzy rules are generated using the history of input/output (I/O) pairs without using any plant model. The generated rules are stored in the fuzzy rule space and updated on-l...

Self-organizing mapping is an unsupervised learning paradigm used in pattern classification and hence artificial intelligence. This paradigm is based on modifying the class features via the incoming input stimuli. Its exciting part is that it introduces concepts such as neighborhood or mapping. Hence the results obtained from this paradigm highly depend on the selected neighborhood and mapping ...

The aim of this special issue is to explore organizing processes in ways which do not assume an a priori existence of space and time. Rather than providing a summary of the papers collected here, this introduction illustrates how Spacing and Timing relate to issues of knowing, organizing, mediation, engagement, alterity and absence/ presence. We examine how various actions and practices may be ...

Artificial neural networks can be used effectively to identify and classify multiple events in a seismic data set. We use a specialized neural network, a self-organizing map, that tries to establish rules for the characterisation of the physical problem. Selected seismic data attributes are used as input patterns, such that the self-organizing map arranges the data in a manner that forms cluste...

This study uses self-organizing feature maps to model the acquisition of lexical and grammatical aspect. Previous research has identified a strong association between lexical aspect and grammatical aspect in child language, on the basis of which some researchers proposed innate semantic categories (Bickerton, 1984) or prelinguistic semantic space (Slobin, 1985). Our simulations indicate that th...

Collaboration of growing self-organizing maps (GSOM) and adaptive resonance theory maps (ART) is considered through traveling sales-person problems (TSP).The ART is used to parallelize the GSOM: it divides the input space of city positions into subspaces automatically. One GSOM is allocated to each subspace and grows following the input data. After all the GSOMs grow sufficiently they are conne...