Part 1: Invited PaperInternational audienceThe history of neural networks can be traced back to the work of trying to model the neuron. Today, neural networks discussions are occurring everywhere. Neural networks, with their remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. A brief history of the neural networks research is presented and some more popular models are briefly discussed. The major attention is on the feed-forward networks and specially to the topology of such the network and method of building the multi-layer perceptrons
A new model is proposed for a content-addressable memory (CAM) based on neural networks. Like the previous Hopfield model, the information is stored in the structure of the network and the read-out procedure may be implemented in the form of an optical vector-matrix multiplier. This model introduces intermediate layers of interneurons between the neuron layers and a dependence of the interconnection weights to a given neuron on the previous history of the neuron. The storage prescription allows each matrix element to have three values instead of only two as in the previous Hopfield model. This more complex model gives better results than the Hopfield model.
Machine-learning techniques frequently predict the results of machining processes, based on predetermined cutting tool settings. By doing so, key parameters of a machined product can be predicted before production begins. Nevertheless, a prediction model cannot capture all the features of interest under real-life industrial conditions. Moreover, careful assessment of prediction credibility is necessary for accurate calibration; aspects that should be addressed through appropriate modeling and visualization techniques. A machine process test problem is proposed to analyze data-visualization techniques, in which a real data set is analyzed that describes deep-drilling under different cutting and cooling conditions. The main objective is the efficient fusion of visualization techniques with the knowledge of industrial engineers. Common modeling and visualization techniques were first surveyed, to contrast standard practice with our novel approach. A hybrid technique combining conditional inference trees with dimensionality reduction was then examined. The results show that a process engineer will be able to estimate overall model accuracy and to verify the extent to which accuracy depends on industrial process settings and the statistical significance of model predictions. Moreover, evaluation of the data set in terms of its sufficiency for modeling purposes will help assess the credibility of these decisions.
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