Method for predicting coaxiality of parts of rotary equipment based on ga-pso-bp neural network

A method for predicting a coaxiality of parts of rotary equipment based on a genetic algorithm-particle swarm optimization-back propagation (GA-PSO-BP) neural network, the method for predicting the coaxiality of the parts of the rotary equipment based on the GA-PSO-BP neural network comprises:

• step 1:
• performing data preprocessing with a coaxiality error source as an input;
• step 2:
• generating an initial population of genetic algorithm solutions, wherein the genetic algorithm solutions correspond to a connection weight and threshold of a BP neural network based on real number coding of individuals, and setting a corresponding evolution algebra, a population size, and crossover and mutation probabilities;
• step 3:
• taking an error between a predicted value and an actual value of the BP neural network as a fitness function, and selecting the individuals from low to high according to the fitness function, wherein a probability of selecting an i-th individual is expressed as:
• P
• i
• =
• l
• i
• ∑
• i
• =
• 1
• l
• i
• ,
• (
• 1
• )
• wherein in the formula (1), li denotes a fitness function value of the i-th individual;
• step 4:
• performing crossover and mutation operations to generate a new population;
• step 5:
• performing decoding by using optimal offspring individuals to obtain an optimal initial weight and threshold;
• step 6:
• initializing a particle swarm, comprising a swarm size, and a position and speed of each particle;
• step 7:
• determining solution ranges of three hyperparameters, comprising a maximum number of times of training, a learning rate, and a regularization coefficient;
• step 8:
• introducing a genetic algorithm to optimize the BP neural network, and performing solving with MSE as an objective function to find an optimal hyperparameter combination;
• step 9:
• introducing the optimal initial weight and threshold and the optimal hyperparameter combination into the BP neural network for training;
• and
• step 10:
• outputting a result of a coaxiality error comprehensively predicted by the GA-PSO-BP neural network.