Polyester spinning process control method based on local plasticity echo state network
A technology of echo state network and polyester spinning, which is applied in the direction of total factory control, total factory control, program control, etc., and can solve problems such as limitations
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Embodiment 1
[0118] A polyester spinning process control method based on a local plastic echo state network, which operates according to the aforementioned steps:
[0119] Initial step size δ (0) is 3*10-6 and the initial mean value of the learning rate parameter m (0) is set to 10-5, the population size is set to 20, and the number of neurons in the reserve pool is set to 300, therefore, the dimension of each individual in the population is 300 dimensions, that is, 300 learning rates, local plasticity The echo state network Ⅰ mainly includes the number of neurons and initial weights of each layer, the sparsity of the reserve pool and the spectral radius. Among them, the number of neurons in the input layer is set to 15, and the number of neurons in the output layer is set to 1. The time is a single-step prediction. The initial weight between the input layer and the reserve pool is randomly generated through a standard normal distribution and scaled by 0.05 times. The initial weight betwe...
Embodiment 2
[0122] A polyester spinning process control method based on a local plastic echo state network, the basic steps are the same as in Example 1, the difference is that the plasticity rule constructed in step (2) is a local Oja rule, wherein the blowing temperature is at the local level of the construction. The optimization process of the initial local plastic echo state network model under the Oja rule is as follows: Figure 5 As shown, after 50 generations of evolutionary optimization, the ESN optimized by local Oja plasticity has a lower prediction error than the ESN optimized by global Oja plasticity, which verifies the effectiveness of local Oja plasticity.
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