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In this paper we present a system which incorporates the features of Fuzzy Qualitative Trigonometry (FQT) with those of the Fuzzy Qualitative Reasoning system, Morven. FQT is designed for modeling and reasoning about robot kinematic systems whereas Morven was designed for simulation and envisionment of straightforward dynamic systems. It has been the case that thus far QR systems have not been designed to reason about the behaviour of dynamic system containing fuzzy trigonometric relations. The resulting tool described in this paper goes some way to addressing this deficit. [ABSTRACT FROM AUTHOR]

Resistance spot welding is a major metal connecting method in vehicle and other domestic electronic domains. Among all the welding techniques, the spot welding inverter is an important direction at the present time. The high nonlinearity and strongly coupled multiple parameters in the resistance spot welding process challenge the classical control theory based on some specific conditions and ideal assumptions, which in real practice obstacle the high-quality welding. This paper put the fuzzy neural network into a constant-current-controlled spot welding inverter, where the welding current peak and its variation are adopted as the input parameters and the duty ratio of the switches is regarded as the output. Eventually a five-layer feed-forward network was constructed, back propagation (BP) algorithm was applied to revise the adjustable parameters in the network, and a mathematical model was established to obtain the training samples serving for the network. The ultimate precision could reach 1.75%, the relative control error is 2.28% with strong external disturbances, the overmodulation is 3.35%, and the total modulating period is seven switching period, which indicated that the proposed algorithm has good performance. [ABSTRACT FROM AUTHOR]

Fuzzy systems properly integrated with Qualitative Reasoning approaches yield a hybrid identification method, called FS-QM, that outperforms traditional data-driven approaches in terms of robustness, interpretability and efficiency in both rich and poor data contexts. This results from the embedment of the entire system dynamics predicted by the simulation of its qualitative model, represented by fuzzy-rules, into the fuzzy system. However, the intrinsic limitation of qualitative simulation to scale up to complex and large systems significantly reduces its efficient applicability to real-world problems. The novelty of this paper deals with a divide-and-conquer approach that aims at making qualitative simulation tractable and the derived behavioural description comprehensible and exhaustive, and consequently usable to perform system identification. The partition of the complete model into smaller ones prevents the generation of a complete temporal ordering of all unrelated events, that is one of the major causes of intractable branching in qualitative simulation. The set of generated behaviours is drastically but beneficially reduced as it still captures the entire range of possible dynamical distinctions. Thus, the properties of the correspondent fuzzy-rule base, that guarantee robustness and interpretability of the identified model, are preserved. The strategy we propose is discussed through a case study from the biological domain. [ABSTRACT FROM AUTHOR]