Patent ID: 11932209
Assignee: CENTRAL SOUTH UNIVERSITY
Field: Measurement (Instruments)
Classification: CPC B  G  Y | IPC B  G

Claim 4:
5. The method for protecting the operation of the train under an air pollution environment according to claim 1, wherein in step 4, the calculation model of pollutant condition for the roof component is trained with an LSTM deep network algorithm, wherein the weight and threshold of the LSTM deep network are obtained by optimization using quantum particle swarm with adaptive weights, comprising:
step Al: using a position vector of each quantum particle individual in quantum particle swarms as the weight and threshold of the LSTM deep network, and initializing the position vector parameter of the quantum particle swarm individual into a random number with a range of [−1, 1];
wherein the number of the quantum particle swarms is in a range of [30, 100], the number of particles in a quantum particle swarm is in a range of [4, 60], the maximum number of iterations is in a range of [300, 1200], the number of iterations for forming an elite swarm is in a range of [50, 200], the premature convergence determination threshold is in a range of [0.02, 0.5], and the worst particle variation ratio 6% among the swarms is in a range of [1%, 6%];
step A2: setting a fitness function, and determining a position vector of an initial optimal quantum particle individual and the number of iterations t, t=1;
substituting the weight and threshold corresponding to the position vector of the quantum particle individual into the calculation model of pollutant condition for the roof component based on the LSTM deep network, determining the type of an identification vector label by using the calculation model of pollutant condition for the roof component based on the LSTM deep network determined from the position vector of the quantum particle individual, and using the reciprocal of the mean square error of the output vector label and the actual vector label as a second fitness function;

step A3: calculating a colony fitness variance of each quantum particle swarm, and performing premature convergence determination;
if the colony fitness variance of the quantum particle swarm is smaller than a premature convergence determination threshold γ, mutating δ% of particles with worst fitness and a colony extreme value particle in the quantum particle swarm, and using the particle with the best fitness currently as a global optimal quantum particle individual;

step A4: determining whether to form an elite swarm;, when the number of iterations is greater than the number of iterations for forming the elite swarm, extracting extreme values of various swarms through information sharing between the swarms to form the elite swarm, and skipping to step A7, otherwise, performing step A5;
step A5: updating particle parameters of the various swarms;
step A6: for each particle, recalculating the fitness value of the particle and comparing the fitness value of the particle with the current individual extreme value of the particle, if the fitness value of the particle is superior to the current individual extreme value of the particle, updating the individual extreme value of the particle; comparing a current colony extreme value of the global extreme value particle with the fitness value of each particle, if the fitness value of a particle is superior to the current colony extreme value, updating the global extreme value particle, assuming t=t+1, and performing step A3;
step A7: determining whether the maximum number of iterations is satisfied, and if the maximum number of iterations is satisfied, exiting the process, otherwise, assuming t=t+1 and performing step A3 till the global optimal value is found; and outputting the weight and threshold of the LSTM deep network.