Download DIFFERENTIAL NEURAL NETWORKS FOR ROBUST NONLINEAR CONTROL by Alexander S. Poznyak;Edgar N. Sanchez;Wen Yu PDF

By Alexander S. Poznyak;Edgar N. Sanchez;Wen Yu

This quantity offers with non-stop time dynamic neural networks idea utilized to the answer of simple difficulties in powerful keep an eye on concept, together with identity, country house estimation (based on neuro-observers) and trajectory monitoring. The vegetation to be pointed out and regulated are assumed to be a priori unknown yet belonging to a given category containing inner unmodelled dynamics and exterior perturbations besides. the mistake balance research and the corresponding mistakes bounds for various difficulties are offered. The effectiveness of the prompt technique is illustrated through its software to numerous managed actual platforms (robotic, chaotic, chemical).

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If we assume, however, that they operate efficiently at an early stage, it’s a good idea to use bidirectional reasoning - a combination of backward and forward reasoning. In this reasoning method, the path of rules leading from the start to the goal state are searched from two directions, from both the start and the goal state at the same time, as it is shown in Fig. 12. The bidirectional reasoning procedure terminates when the reasoning "bridge" seen in the Figure is built up. 5. SEARCH METHODS As it was mentioned earlier, reasoning problems are solved by search on the reasoning graph in the state-space.

In this case we look for an implication with its consequence part containing the predicate to be proved. Thereafter we prove the predicates in the condition part of this implication. This is called backward reasoning, because it uses modus ponens backward. In case of both directions a reasoning path, that is a chain of rules can be constructed between the facts and the goal state. This reasoning chain can be seen as a path in the state-space, a sequence of rules leading from one state to another.

The properties and relationships of the knowledge objects and classes are described by a directed graph. The vertices of the graph correspond to the objects and their attributes or properties: the labelled edges depict the relationships between the vertices. Most of the relationships in a semantic net fall into pre-defined categories. The most common relationships are as follows. is-a which means that objectA is an instance of objectB if the relationship objectA is_a objectB holds. part_of meaning that objectA is a part of or an attribute of objectB when objectA part_of objectB holds.

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