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In order to predict the performance of a manufacturing process or system, proper mathematical models are needed. This research investigates the use of two competitive unsupervised data mining methods - regression and neural networks - in developing an empirical model for two electronics fabrication processes/systems. A case study from experimental data of electronics fabrication is used to demonstrate...
The theory and applications of artificial neural networks have developed rapidly since the mathematical model of neuron was presented, but the design of network structure for a certain problem was a roadblock over a long period of time. In 1990s, the covering algorithm for forward neural network was put forward, this algorithm is a constructive machine learning method, it designs network with sphere...
Recently, the rule-based approach, the graph-based approach, the hint-based approach, the artificial neural networks based approach and the volume decomposition approach are the common feature recognition techniques available today. This work discusses a neural network approach for features recognition from B-rep solid modeler, which has significant effect on improving working efficiency in the product...
Recently, the rule-based approach, the graph-based approach, the hint-based approach, the artificial neural networks based approach and the volume decomposition approach are the common feature recognition techniques available today. This work discusses a neural network approach for features recognition from B-rep solid modeler, which has significant effect on improving working efficiency in the product...
Water pollution has posed a severe problem in modern society. Evaluation of water quality is a meaningful topic today. To identify the specific water category and predict the water quality in the future, a particle swarm optimization (PSO) based artificial neural network (ANN) approach is presented. The data investigated from the Yangtze River are chosen as the original cases to construct the ANN...
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