GA-BASED PID NEURAL NETWORK CONTROL FOR MAGNETIC BEARING SYSTEMS

In order to overcome the system non-linearity and uncertainty inherent in magnetic bearing systems, a GA（genetic algnrithm）-based PID neural network controller is designed and trained tO emulate the operation of a complete system （magnetic bearing, controller, and power amplifiers）. The feasibility of using a neural network to control nonlinear magnetic bearing systems with unknown dynamics is demonstrated. The key concept of the control scheme is to use GA to evaluate the candidate solutions （chromosomes）, increase the generalization ability of PID neural network and avoid suffering from the local minima problem in network learning due to the use of gradient descent learning method. The simulation results show that the proposed architecture provides well robust performance and better reinforcement learning capability in controlling magnetic bearing systems.

Optimization of Development Intensity Index of Regulatory Land under the Constraint of Bearing Capacity of Road Network:A Case Study of Xingtang County

In the process of urban development in China,the vast majority of urban construction is faced with the prominent contradiction between scarce land resources and vigorous construction demand.Moreover,high-density and high-intensity development is ubiquitous.However,the overall development amount of a city is restricted by the bearing capacity of road network to some extent,and there is an upper limit.Based on this,Xingtang County of Shijiazhuang City is taken as the research object,and bearing capacity of road network is selected as research emphasis.With the aid of traffi c planning software TransCAD,simulation and quantitative analysis are conducted,and traffi c demand is forecasted,to analyze impact relationship between land-use planning and traffic planning in regulatory planning.It facilitates later modifi cation and optimization of volume rate in the land development intensity index,thus providing rational basis for programme adjustment,preparation and management of regulatory planning in Xingtang County.

Quantitative Diagnosis of Fault Severity Trend of Rolling Element Bearings

The condition monitoring and fault diagnosis of rolling element bearings are particularly crucial in rotating mechanical applications in industry. A bearing fault signal contains information not only about fault condition and fault type but also the severity of the fault. This means fault severity quantitative analysis is one of most active and valid ways to realize proper maintenance decision. Aiming at the deficiency of the research in bearing single point pitting fault quantitative diagnosis, a new back-propagation neural network method based on wavelet packet decomposition coefficient entropy is proposed. The three levels of wavelet packet coefficient entropy（WPCE） is introduced as a characteristic input vector to the BPNN. Compared with the wavelet packet decomposition energy ratio input vector, WPCE shows more sensitive in distinguishing from the different fault severity degree of the measured signal. The engineering application results show that the quantitative trend fault diagnosis is realized in the different fault degree of the single point bearing pitting fault. The breakthrough attempt from quantitative to qualitative on the pattern recognition of rolling element bearings fault diagnosis is realized.