The difference between the calculations. According to the actual system, the neural network identification model is designed. The model established by this method is positive. The compressive low viscosity and heat sensitivity make the characteristics of the pneumatic feeding system difficult to grasp. In summary, the pneumatic servo system has the following Characteristic nonlinear function, parameter estimation method only uses polynomial, therefore, in a sense, neural network identification is the promotion and improvement of parameter estimation method.
The effect of neural network identification is related to the selected area model and the method of calculating the weight. In various neural network models, the time-varying, ie, the system parameters are not constant, change with time and during the movement, The parameters are related to the location.
2 The sensitivity of the thermal system characteristics is severe.
3 The characteristics of the pressure sensitive system are greatly affected by the pressure fluctuation of the air source.
4 The large compressibility of the nonlinear gas and the friction of the actuator cause the system characteristics to be strictly non-linear.
8 to 8 coffee 1 network structure simple learning convergence accuracy, very, the identification of the output system of the integration of the core 1 so the text selected this model to identify the switch valve controlled pneumatic servo system.
The three-part network model Yang network model is only 1 hidden, 1 output. Each of these features makes the model of the pneumatic servo control system difficult to obtain, and brings many difficulties to the control of the pneumatic system. The mathematical model of the controlled system is very important for the analysis and control of the system. The methods for establishing dynamic system mathematical models are mechanism modeling and experimental modeling. Identification modeling methods include step response method frequency response method and analysis method and parameter estimation method 1. In recent years, the progress made by the neural network research institute provides a new method for dynamic system identification, and many successful applications have been reported. The difference between neural network identification and parameter estimation method is essentially the same. Both of them use the input and output signals to meet the å…‘ å…‘ å…‘ method to find the model that can reflect the system characteristics. The difference is that the neural network is more active than the parameter estimation method, and can be close to the total non-linear å¿ function, reflecting the characteristics of any nonlinear system. Have self-learning and memory ability; the neural network uses all nodes to output according to Xing's law, 1.
The network output identification model is quite different from the actual object.
Edit Zhang Xinlong X input vector; C1 first. Only 8 centers that concealed the point, the factory idle number is usually 0 dip type function, that is, 4 based on the KBF network dynamic identification principle ut system output and output version 1 network identification. Welcome to the normal selection, system input, the first value before the moment and the output time before the first 伉 as 1; the input vector of the training network. Using the learning algorithm to use the sample to train only 8 networks, the weight of the 1st line is identified as the solid feature of the wash, during training. Index number of eucalyptus values; v network output value V learning 4 rate due to 5 based on the 881 network model of the switch valve control pneumatic position servo mechanism analysis modeling 4, switch valve control pneumatic position what is the system is the order system. Therefore, the design of the 1 neural network model to identify the maximum and minimum values ​​of such systems is called this value. The algorithm of Equation 45 is used to obtain the weight of each hidden node 51.
Output. Curve 1 is a real small output. Curve 2 is the model imitation 讧 output from the same knowledge of both Qimu. The model built by this ash is absolutely correct, and the 6 conclusions serve the system to escape the identification research. Several conclusions are drawn: 1. The number of hidden nodes in the neural network should not be too small.
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