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Proper initialization is one of the most important prerequisites for fast convergence of feed-forward neural networks like high order and multilayer perceptrons. This publication aims at determining the optimal value of the initial weight v ariance (or range), which is the principal parameter of random weight initialization methods for both types of neural networks.An overview of random weight initialization methods for multilayer perceptrons is presented. These methods are extensively tested using eight real-world benchmark data sets and a broad range of initial weight v ariances by means of more than 30 000 simulations, in the aim to nd the best weight initialization method for multilayer perceptrons.For high order networks, a large number of experiments (more than 200 000 simulations) was performed, using three weight distributions, three activation functions, several network orders, and the same eight data sets. The results of these experiments are compared to weight initialization techniques for multilayer perceptrons, which leads to the proposal of a suitable weight initialization method for high order perceptrons.The conclusions on the weight initialization methods for both types of networks are justi ed by su ciently small con dence intervals of the mean convergence times.

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With widely used concurrent and collaborative engineering technologies, the validity and consistency of product information become important. In order to establish the state of the art, this paper reviews emerging concurrent and collaborative engineering approaches and emphasizes on the integration of different application systems across product life cycle management (PLM) stages. It is revealed that checking product information validity is difficult for the current computer-aided systems because engineering intent is at best partially represented in product models. It is also not easy to maintain the consistency among related product models because information associations are not established. The purpose of this review is to identify and analyze research issues with respect to information integration and sharing for future concurrent and collaborative engineering. A new paradigm of research from the angle of feature unification and association for product modeling and manufacturing is subsequently proposed.Keywords Concurrent and collaborative engineering · Feature-based design and manufacturing · Product life cycle modeling · Information validity and consistency

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