# Reactor Temperature Prediction Method Based on CPSO-RBF-BP Neural Network
## 2 RBF-BP Combined Neural Network Model
### Algorithm for Chaotic Particle Swarm (CPSO)  
(1) Particle swarm method  
The particle swarm optimization algorithm [24; 25] (PSO) was inspired by a new swarm intelligence optimization technique that models animal foraging behavior, such as that of birds [26]. Its main characteristics are fast convergence, good global search capabilities, and the capacity to overcome system lag. Based on the research on the predation behavior of birds, the strategy of birds in searching for food is to find the nearest bird range to the food. These birds are called \"particles\", and the particles have their position and speed. Each particle will follow the optimal particle to search in space. The idea that each particle can share the current search result is very simple and easy to implement. However, the disadvantage of PSO is that it is easy to converge quickly, so there will be a disadvantage of low accuracy. If the coefficient is accelerated, the particle easily misses the optimal solution. If, in the process of convergence, all the particles fly to the optimal solution, the particles will lose diversity and will slow down. It can locate persons who satisfy the conditions by constantly changing its search mode in the solution space [27]. A set of particles is generated at random, and each particle represents a viable solution to the problem, each particle has its fitness, and fitness determines the degree of superiority and inferiority. Throughout the solution process, the particle will continuously update its velocity and position based on the individual and global extremes to find the best solution. \[ \begin{split} V_{id}^{k+1}=V_{id}^{k}+c_{1}\times r_{1}\times\left( P_{id}^{k}-X_{id}^{k}\right)+c_{2}\times r_{2}\times\left(P_{gd}^{k}-X_{id}^{k} \right)\\ \hskip 28.452756 ptX_{id}^{k+1}=X_{id}^{k}+V_{id}^{k+1}\end{split}  \tag{6}\]  
where: \(i=1,2,\ldots,N;\mathrm{d}=1,2,\ldots,M\). \(K\) is the number of iterations; \(c_{1}\) and \(c_{2}\) are learning factors; And \(r_{1},r_{2}\) is a random number between. (2) Algorithm for chaotic particle swarm  
To avoid the standard particle swarm algorithm sliding into local optimality and premature aging [28], the system can be modified to show chaotic features by adding the update adjustment process of particle velocity using the chaotic algorithm's ergodicity and regularity. The chaotic particle swarm algorithm (CPSO) seeks to achieve global optimization by substituting a chaotic sequence for the normal random sequence. The basic idea of the CPSO is as follows:  
1. Chaotic initializes the particle positions in the population. Based on the chaotic motion characteristics, selecting individuals with high fitness as the initial population can improve search efficiency. 2. Carry out a chaotic search on the top 20% of the population with high fitness, generate the corresponding chaotic sequence through Logistic mapping, and the search can be carried out in the field. Once there is a better individual extremum position, the local search ability of the algorithm can be improved by replacing it. 3. To improve the optimization performance of the algorithm, the main parameter selection and method of the algorithm were improved. In this paper, the inertia weight was linearly decreased, which can have a strong optimization ability in the early stage and can be carefully searched locally in the later stage. The formula is as follows:  
\(\omega_{\mathrm{max}}\) represents the maximum value of the weight factor, \(\omega_{\mathrm{min}}\) represents the minimum value, the maximum value is usually 0.9, the minimum value is 0.4, t and t max are the current iterations and the maximum iteration times. Logistic mapping formula: \(z_{t+1}=\mu z_{t}(1-z_{t})\)  
\(\mu\) is the control variable, \(z_{t}\) is the chaotic sequence.