Fuzzy networks and neural networks offer two different approaches of nonlinear black box modelling. Efficient identification methods have been developed to calculate the parameters for a given structure and have been applied successfully in many examples. But the applications proposed in the literature usually miss the comparison of the alternative method, so that the selection of the more suitable approach for a given task is difficult. This paper aims to ease the decision for one of the two methodologies by considering one well-known high quality approximator of each network type, and presenting a fair comparison. For this purpose, two mathematical and three complex technical examples of nonlinear systems are considered. Generally, fuzzy networks and neural networks face the problem of overtraining causing poor validation/generalisation results. A modification of the established identification methods is proposed as a significant improvement for both approaches.

The effects of the management of straw, nitrogen fertilization, and application rates on the dissipation of metsulfuron-methyl, methabenzthiazuron, and trifluralin were investigated in a 3-year field crop rotation study, complemented with laboratory studies. The dehydrogenase activity in soil was measured as an indicator of the soil microflora. The effects were specific for the herbicides investigated. The amendment of straw accelerated the dissipation of trifluralin and methabenzthiazuron. Furthermore, the movement of trifluralin, but not methabenzthiazuron, in the top 30 cm of the soil was reduced. Nitrogen fertilization and high application rates significantly decreased only the dissipation of methabenzthiazuron in the laboratory. Dehydrogenase activity in soil was influenced mainly by the amendment of straw. There was no evidence for cumulative effects on herbicide dissipation due to crop management within the crop rotation. Keywords: Herbicides; degradation; straw incorporation; nitrogen fertilization; application rate

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The performance of model-based controller design relies heavily on the quality and suitability of the utilized process model. This contribution proposes a fuzzy network based nonlinear controller design methodology. Fuzzy networks are a model approach combining high approximation quality with high interpretability. The input/output (I/O) models commonly used for identification are transformed to fuzzy state-space models. Transferring and adjusting methods from linear state-space theory a control concept consisting of a fuzzy state controller and an adaptive set-point filter for nonlinear dynamic processes is deduced. The capability of the method is demonstrated for a hydraulic drive.

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