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Genetic Neural Network package to improve sensor performance in the application


. 51.11461.73382.25792.78083.30953.83694.371371.1754 1.88142.45083.01833.59014.16284.7356 Table 2 Genetic neural network training output 3.03.544.555.566.57 desired output 0.20.2020.2080.2l30.2120.2220.2l60.2190.2260.2180. 40.3950.3860.3770.3820.3790.39l0.3870.38l0.390 to 0.1,0.3, 0.5,0.6 MPa pressure of the current and sensor output voltage as learning samples, the standard pressure value as a learning sample of the desired output. Will work under 0.2 and 0.4MPa pressure sensor output current and input voltage as the test samples for genetic neural network computing, network output of the test samples shown in Table 2. Integration of the measured pressure values ​​can be seen that the relative fluctuations are: a m-a-xl-API (5) port p --- a LJ fluctuations in the measured pressure for the maximum absolute value; P is the pressure sensor system measured full-scale value. According to data in Table 2 were P-5.7 port,vibram five fingers outlet, the sensor system stability greatly improved. 4 Conclusion genetic neural networks as a new analysis of the problem in all aspects of scientific research has been applied. In this paper, based on genetic neural network approach to improve sensor performance, and produce a common genetic neural network package. With CYJ-101-type piezoresistive pressure sensor system measuring the measured data input genetic neural network training, integrated sensor before full-scale output current fluctuation was 55, after the integration of current volatility of the measured pressure reduced to 5.7 . Thus, the use of genetic neural network package can effectively improve the performance of the sensor,tods shoes outlet, and the package of practical, users need to understand neural network knowledge can be (the first turn 8O page) ink ink! 11 Dan environment under the common browser design a digital PID chart fast, there are hysteresis (dead band) stability control and anti-jamming capability. Implement these algorithms, conventional PID can be on the basis of an expert system to increase, in a different section, under the control of rules, select a different PID parameters or directly change the output. Therefore,herve leger sale, we designed an intelligent module, to achieve these control rules. In VerilogHDL language used to describe a series of if statements. Figure 2 is a top-level block diagram of intelligent digital PID. Comprehensive, with Max + PluslI compile and download. The chip used in industrial heating automatic temperature control system, and achieved good results. By the: J = FLEX1OK series chip SRAM technology, you can dynamically download. So, for different systems, can easily change the rules of intelligent control module, in order to adapt to different systems. Figure 2 top-level block diagram of intelligent digital PID 4 Conclusion We chose Altera's FIEX10K30RC208-3 core E41 slice. Under the SynplifyPro7.0 complete the design and use VerilogHDL Ell [2-1 [3] References mine together over. A new adaptive PID control algorithm [J]. Industrial Instrumentation & Automation, 2002, (5) :23-25. Tangong Jian, et al. Hou heating steam temperature regulation in the production of intelligent control [J]. Industrial Instrumentation & Automation, 2003, (1) :13-14. Liuming Lan, et al. Rule-based expert intelligent PID controller [J]. Wuhan University of Automobile Industry, 1997,19 (2) :44-47. Song Wanjie, et al. CPID technology and its applications [M]. Xi'an. Xidian University Press, 1999. Author Rauch into men, born in 1963,herve leger toronto, Associate Professor. Research interests include computer measurement and control. (Continued from page 78) using a software package for data fusion, easy to spread. References [1] Li Xiaoan. Introduction to neural networks and neural computers [M]. Xi'an: Northwest Industry University Press, 1994.8O [2] [3] Chen Guoliang, ZHUANG Zhen-quan,MBTシューズ 販売店, et al. Genetic algorithm and its application [M]. Beijing: People's Posts and Telecommunications Press, 1996. Chang Bingguo, Liu Junhua. Using sensor data fusion technology to improve the reliability of [J]. Xi'an Jiaotong University, 1998,32 (12) :5-8.
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