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The time series prediction model based on neural network can perfectly reflect the trend of development of nonlinear system, but the training speed for neural network is very slow, therefore, it is easily prone to local extremum. So we come up with a learning algorithm combining genetic algorithm and BP algorithm for the training of BP neural network, to realize optimization of network structure....
With the e-commerce market competition becoming more and more furious, it has become one of the focuses of companies that how to avoid customer churn and carry out customer retention. This paper applies many techniques of data mining to the research of customer churn, such as clustering analysis, decision tree, neural network, etc, establishes an e-commerce customer churn model and analyzes the factors...
When the holes to be machined are mass produce with numerical control machine tool, the empty routing will numerous and the process is inefficient. The machining routing in quick process was proposed in this paper and the optimizing mathematical models for process routing in machining a group of holes was established. A new method was presented by combined improved genetic algorithm (GA) with Elman...
Parameters control problem was crucial in rolling industrial, but the mechanical properties forecasting of strip steel was an information space incompletely and non-linear complex system which was hard for traditional method. Artificial neural networks was a non-linear system with strong non-linear modeling ability, but the traditional BP neural networks has many shortcomings like easily step into...
The artificial neural networks with structure self-organizing algorithm was used in quality testing of gear wheel, the structure self-organizing training algorithm was proposed, the application in quality testing of gear wheel was analyzed and the simulation shows the method is effective and can applied into mechanism quality testing system with the automation realized.
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