A bp neural network pid control algorithm based on variant particle swarm optimization
A BP neural network and mutant particle swarm technology, applied in the direction of adaptive control, general control system, control/regulation system, etc., can solve problems such as easy to fall into local optimum, unsatisfactory control effect, slow optimization speed, etc., to overcome The effect of backpropagation error correction method, global search capability enhancement, and algorithm robustness improvement
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[0041] Such as Figure 1-3 A BP neural network PID control algorithm of variation particle swarm optimization shown includes the following steps:
[0042] Step 1. Initialize the BP neural network: initialize the BP neural network, determine the number N of nodes in the input layer of the BP neural network and the number S of nodes in the hidden layer, and give the initial value w of the hidden layer weighting coefficient ij and the initial value of the output layer weighting coefficient w jo ; The number of nodes in the input layer of the BP neural network corresponds to the selected operating state of the controlled system, the integral function of the output layer neurons is a non-negative Sigmoid function, and the activation function of the hidden layer neurons is a positive and negative symmetric Sigmoid function .
[0043] Step 2. Initialize the variation particle swarm optimization algorithm: initialize the variation particle swarm optimization algorithm, determine the...
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