Zhang Yuanmin

Genetic Algorithm Optimizing Neural Network for Short-Term Load Forecasting

Short-term load forecasting in power system is necessary for management and contrl of power system. A new method for short-term load forecasting was presented based on neural networks optimized by genetic algorithm (GA) is proposed in this paper, short-term load forecasting model for power system was setup as sample sets for Elman neural network(Elman NN), with GAs optimizing and Elman NNs dynamic feature, the higher forecasting pricision was realized and the simulation indicates the method is feasible and effective.

Application of Genetic Algorithm Optimizing Neural Networks in Machining a Group of Holes

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 neural networks in routing optimization in order to enhance the process efficiency, the structure and learning algorithm of neural networks was introduced and GA operation steps was also presented, the simulations indicates this new method was feasible and effective.

Application of Sensitivity Pruning Neural Networks in Surface Roughness Prediction

The surface roughness is a key parameters in high speed machining and often hard to control. The prediction model for surface roughness was created based on artificial neural networks which have strong non-linear modeling ability. The sample data collection method was analyzed and BP neural networks was designed, but the traditional BP neural networks has many shortcomings like easily step into local minimum, with weak generalization ability and the middle layer neuron are hard to determine, so the sensitivity pruning algorithm applied. The simulation shows the method is effective and can provide a guidance to optimize cutting parameters and control surface quality.

Application of Structure Self-organizing Neural Networks for Quality Testing of Gear Wheel

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.