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Intelligent optimization algorithm Neural network prediction Radar communication Wireless sensor Power system

Signal processing Image processing Path planning Cellular automaton Drone

### Content introduction

With the advent of the big data era, data classification prediction plays an increasingly important role in various fields. BP neural network is a commonly used classification prediction algorithm, but it is easy to fall into the problem of local optimal solutions during the training process. In order to improve the performance of BP neural network, researchers have proposed many optimization algorithms, among which particle swarm optimization algorithm is a widely used method.

The particle swarm optimization algorithm is an intelligent optimization algorithm that simulates the foraging behavior of a flock of birds. It simulates information exchange and cooperative behavior among individuals in a flock of birds to find the optimal solution. In the data classification prediction applied to BP neural network, the particle swarm optimization algorithm can be used to optimize the weights and thresholds of the BP neural network, thereby improving its classification accuracy.

Specifically, the particle swarm optimization algorithm searches for the optimal solution by iteratively updating the position and velocity of particles. In each iteration, the particle adjusts its position and speed based on its own position and speed information, as well as the guidance of the global optimal solution and the individual optimal solution. In this way, the particles gradually approach the optimal solution and find the best combination of weights and thresholds in the search space, thereby improving the classification performance of the BP neural network.

In the process of using particle swarm optimization algorithm to optimize BP neural network, you need to pay attention to the following points. First, the size of the particle swarm and the number of iterations need to be appropriately selected to fully search the entire solution space. Second, an appropriate fitness function needs to be defined to evaluate the performance of each particle. The fitness function can be measured using indicators such as classification accuracy and sum of squared errors. In addition, appropriate inertial weights and acceleration factors need to be set to balance the capabilities of global search and local search.

It is worth mentioning that the particle swarm optimization algorithm is not the optimal solution algorithm that is absolutely suitable for all problems. In practical applications, it is necessary to select an appropriate optimization algorithm based on the characteristics of the specific problem. In addition, other algorithms, such as genetic algorithms, simulated annealing algorithms, etc., can also be combined to further improve the performance of BP neural networks.

In short, optimizing the data classification prediction of BP neural network based on particle swarm optimization algorithm is an effective method. By reasonably setting parameters and fitness functions, the particle swarm optimization algorithm can help BP neural networks overcome the problem of local optimal solutions and improve classification accuracy. In the future, we can further study and improve the particle swarm optimization algorithm to address data classification prediction challenges in different fields.

### Part of the code

%_______________________________________________________________% % Whale Optimization Algorithm (WOA) source codes demo 1.0 % % % % Developed in MATLAB R2011b(7.13) % % % % Author and programmer: Seyedali Mirjalili % % % % e-Mail: [email protected] % % [email protected] % % % % Homepage: http://www.alimirjalili.com % % % % Main paper: S. Mirjalili, A. Lewis % % The Whale Optimization Algorithm, % % Advances in Engineering Software, in press, % % DOI: http://dx.doi.org/10.1016/j.advengsoft.2016.01.008 % % % %_______________________________________________________________% % This function initializes the first population of search agents function Positions=initialization(SearchAgents_no,dim,ub,lb) Boundary_no= size(ub,2); % number of boundaries % If the boundaries of all variables are equal and user enter a signle % number for both ub and lb if Boundary_no==1 Positions=rand(SearchAgents_no,dim).*(ub-lb) + lb; end % If each variable has a different lb and ub if Boundary_no>1 for i=1:dim ub_i=ub(i); lb_i=lb(i); Positions(:,i)=rand(SearchAgents_no,1).*(ub_i-lb_i) + lb_i; end end

### Run results

### References

[1] Chen Jiabing, Wu Ziyin, Zhao Dineng, et al. Brief PSO-BP seafloor acoustic sediment classification method based on particle swarm optimization algorithm [J]. Acta Oceanographica Sinica, 2017.

[2] Wang Yuyuan. Research on neural network model prediction strategy based on PSO-BP algorithm [J]. Electronic Quality, 2012(3):3.DOI:10.3969/j.issn.1003-0107.2012.03.002.

[3] Wang Yunjing, Wang Qingtian, Liu Yaxin, et al. A short-term photovoltaic output prediction method, device and storage medium based on LVQ-PSO-BP neural network. CN202211340551.3[2023-10-02].