A new method to detect damages on crates of beverages is investigated. It is based on a pattern-recognition-system by an artificial neural network (ANN) with a feedforward multilayer-perceptron topology. The sorting criterion is obtained by mechanical vibration analysis which provides characteristic frequency spectra for all possible damage cases and crate models. To support the network training, a large number of numerical data-sets is calculated by the finite-elementmethod (FEM). The combination of artificial neural networks with methods of numerical simulation is a powerful instrument to cover the broad range of possible damages. First results are discussed with respect to the influence of modelling inaccuracies of the finite-element-model and the support of the ANN by training-data obtained from numerical simulation. Also the feasibility of neuro-numerical ANN training will be dwelled on.
Fuzzy min-max neural network (FMN), proposed by Simpson is a well-known supervised neuro-fuzzy classifier that has been successfully used by many researchers for pattern recognition. However, the FMN represents the learned knowledge with exhaustive details in a `fine-grained' manner that reduces its performance for pattern recognition in terms of the recall time per pattern. In this paper, we adapt the basic architecture of the FMN to represent the learned knowledge in a compact way that is in a `coarse-grained' manner, which is closed to human thinking. The working of the proposed method that is fuzzy min-max neural network with knowledge compaction (FMN-KC) is illustrated using the Fisher Iris dataset. The potential of using the FMN-KC for supervised outlier detection is demonstrated using a time-series disk defect dataset published by NASA and KDD cup 99 dataset available in UCI repository. The proposed method achieves around 50% gain in the recall time as compared to the original FMN and the recognition rate is also comparable. We strongly recommend using the proposed architecture FMN-KC for supervised outlier detection in the real time applications, where recall time per pattern is one of the key parameters.