## 4 Process Neural Network Model Optimized by ILoTFWA
### Validation of ILoTFWA-CPPNN Algorithm Model  
In this paper, 130 sets of steady-state asymmetric operation data for a No. 1 300 WM turbo-generator in a power plant, over a period of six months (March, April, May, June, July and August in 2019), were selected. Moreover, 104 samples constituted the training set and 26 samples constituted the test set. Comprehensively considering the design manual, daily operation data, maintenance records, and engineer's experience of a large generator, a three-phase current versus time curve, spatial geometric position versus time curve of main rotor components, thermal conductivity versus time curve, and conductivity versus time curve of rotor slot wedge were all selected as the input parameters. Through experimental comparison and analysis, the CPPNN network structure was selected as 6-15-1; that is, 6 input nodes, 15 process neuron nodes in the competitive layer, and 1 non-time-varying neuron output node. The output value is the maximum temperature rise of the main components of the rotor. The Walsh function was selected as the basis function. When the basis function item L = 16, the fitting accuracy of the input function was 0.01. Error accuracy was \(\varepsilon\) = 0.05 and maximum learning times M = 10,000. The ILoTFWA algorithm proposed in this paper was used to optimize the weight and threshold of the CPPNN, and the network converged after 2793 training cycles. Using the trained ILoTFWA-CPPNN model to calculate the samples in the training sample set, the average relative errors were 2.81%, respectively. The average relative error of 26 samples in the test set was 2.65%, which is a good result in the current prediction of rotor temperature rise. In order to further verify the prediction performance of the model, the same data set was used to train the classical CPPNN, the genetic algorithm-improved CPPNN (GA-CPPNN), the particle swarm optimization algorithm-improved CPPNN (PSO-CPPNN), and the classical fireworks algorithm-improved CPPNN (FWA-CPPNN). At the same time, according to the specific optimization objectives of the weight and threshold and combined with the relevant experimental results, the key parameters of the three algorithms were set as follows. The population size of the classical fireworks algorithm was \(n\) = 70, the adjustment constant of fireworks explosion radius was \(d\) = 5, the adjustment constant of the firework explosion spark number was \(c\) = 40, the upper limit of the firework explosion spark number was \(ub\) = 0.8, the lower limit of the firework explosion spark number was \(lb\) = 0.04, the number of Gaussian variation sparks was \(g\) = 5, and the maximum number of iterations was \(T\) = 1000. The parameters of the GA were: population size \(popu\) = 30, genetic algebra \(gen\) = 100, crossover probability \(pcross\) = 0.8, and mutation probability \(pmutation\) = 0.05. For the PSO, the parameters were as follows: speed update parameter \(c_{1}\) = \(c_{2}\) = 1.494 45, evolution number \(maxgen\) = 150, population size \(sizeof\) = 30, individual maximum \(popmax\) = 7, individual minimum \(popmit\) = \(-\)7, individual maximum speed \(v_{max}\) = 1, and individual minimum speed \(v_{min}\) = \(-\)1. For the parameter selection in the standard CPPNN network prediction model, the same parameters as the ILoTFWA-CPPNN model in this paper were used. For the steady-state experiment under asymmetric operating conditions, 300 MW generators of a power plant were used in the experiment as shown in Figure 7, and the details of the experiments are given in [45]. Some measured temperature-rise data of the rotors and the comparison results of various prediction models are shown in Table 3.  
Table 3 indicates that the ILoTFWA-CPPNN method shows better performance in predicting the maximum temperature rise of the rotor, both in terms of the accuracy and learning time.