By Anton Andrejko, Mária Bieliková (auth.), Véra Kůrková, Roman Neruda, Jan Koutník (eds.)

This quantity set LNCS 5163 and LNCS 5164 constitutes the refereed court cases of the 18th overseas convention on synthetic Neural Networks, ICANN 2008, held in Prague Czech Republic, in September 2008.

The 2 hundred revised complete papers awarded have been conscientiously reviewed and chosen from greater than three hundred submissions. the second one quantity is dedicated to trend reputation and information research, and embedded platforms, computational neuroscience, connectionistic cognitive technological know-how, neuroinformatics and neural dynamics. it additionally comprises papers from detailed classes coupling, synchronies, and firing styles: from cognition to disorder, and confident neural networks and workshops new traits in self-organization and optimization of synthetic neural networks, and adaptive mechanisms of the perception-action cycle.

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2 3 3 ObjectA 4 ObjectA ObjectB ObjectB Fig. 3. Taxonomy distance for objects ObjectA and ObjectB is computed. Common part (nodes) in the taxonomy is emphasized by dotted arrow; solid arrow is used to show longer distance from the root node. 0. Identification of relevant pairs using only the object’s label is not satisfactory. Each object in the ontology can have a label that could be compared using selected data type metrics. Since the label is optional and does not have to necessarily express any semantics we avoid using it.

Identification of relevant pairs using only the object’s label is not satisfactory. Each object in the ontology can have a label that could be compared using selected data type metrics. Since the label is optional and does not have to necessarily express any semantics we avoid using it. It should be noted that for automatically acquired instances it is obvious that meaningful labels are not present. We proposed the similarity measure to identify pairs of objects, therefore, a relevance matrix is constructed which size is specified by cardinalities of sets of objects.

Inverse M od el + r(t+ 1 ) - Existing C o ntro ller + + u (t) Plant y (t+ 1 ) Fig. 1. Block diagram of the Additive Feedforward Controller The principle of additive feedforward control is quite simple: add to an existing (but not satisfactory functioning) feedback controller an additional inverse process controller. The additive feedforward control strategy offers the following important advantages [10]: – Data collecting can be done using the existing closed loop, avoiding plant stopping for data collection and facilitating the access to good quality data.

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