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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
In the field of modern science and technology, state estimation is a very important task. It can help us understand and predict the behavior of the system, thus providing us with better decision-making basis. Particle filter (PF) and particle swarm algorithm (PSO) are two commonly used algorithms, which play an important role in state estimation. This article will introduce how to use particle swarm optimization particle filtering (PSO-PF) to achieve state estimation, and compare the effects before and after the algorithm process.
First, let us briefly understand the basic principles of particle filtering (PF) and particle swarm optimization (PSO). Particle filtering is a state estimation algorithm based on the Monte Carlo method, which estimates the system state through a group of particles. Each particle represents a possible state of the system. Through continuous updating and resampling, particle filtering can approximate the true state of the system. The particle swarm algorithm is a simulated evolutionary algorithm that simulates the foraging behavior of a flock of birds and finds the optimal solution through collaboration and information exchange among individuals. The particle swarm algorithm continuously adjusts the speed and position of particles to find the optimal solution.
Next, we will introduce how to apply the particle swarm algorithm to particle filtering to optimize the effect of state estimation. First, we need to define a fitness function to evaluate the state estimation effect of each particle. Then, we use the particle swarm algorithm to adjust the speed and position of the particles so that the fitness function reaches the optimal value. By continuously iterative optimization, we can get a better state estimation result.
In practical applications, we found that using particle swarm optimization to optimize particle filtering (PSO-PF) can significantly improve the accuracy and robustness of state estimation. Compared with traditional particle filtering, PSO-PF has better performance in finding the global optimal solution and can avoid falling into the local optimal solution. In addition, PSO-PF can converge to the optimal solution faster, thus saving computational time.
Finally, let us compare the effects before and after the PSO-PF algorithm process. Through actual state estimation cases, we can clearly see that the particle filter optimized by the particle swarm algorithm has significantly improved the state estimation accuracy and convergence speed. This shows that the particle swarm algorithm can effectively optimize the particle filter algorithm, thereby improving the effect of state estimation.
In summary, particle swarm optimization-based particle filtering (PSO-PF) can play an important role in state estimation. It can not only improve the accuracy and robustness of state estimation, but also speed up the convergence speed of the algorithm. Therefore, in practical applications, we can consider using the PSO-PF algorithm to achieve state estimation to obtain better results.
Part of the code
clear all;</code><code>clc;</code><code>close all;</code><code>%----------------- -------------------------------------------------- ---</code><code>?</code><code>%---------------------------------- ---------------------------------------</code><code>%--- --------------------------System parameters---------------------- ------------</code><code>x=0.1;%Initial state</code><code>Q=10;%Process noise</code><code>R=1 ;%observation noise</code><code>time=100;%simulation time</code><code>P=5;%variance initial value</code><code>N=100;%number of particles</code><code>%-----------------------------Initial sampling-------------- ---------------------</code><code>for i=1:N</code><code> xpart(i,1)=x + sqrt(P)*randn;</code><code>end </code><code> </code><code>for k=2:time </code><code> %------- -------------------Simulation System----------------------------- ------</code><code> % equation of state</code><code> x=0.5 * x + 25 * x / (1 + x^2) + 8 * cos(1.2*(k- 1)) + sqrt(Q) * randn;</code><code> % Observation equation</code><code> y = x^2 / 20 + sqrt(R) * randn;</code><code> %Save x value</code><code> x_true(k)=x;</code><code> %-------------------------- ---Particle filter----------------------------------</code><code> [x_hat( k),xpart(:,k)]=PF(N,xpart(:,k-1),k,Q,R,y);</code><code> %--------- ---------------Particle Swarm Particle Filter------------------------------- -</code><code> [x_hat1(k),xpart(:,k)]=PSO_PF(N,xpart(:,k-1),k,Q,R,y);</code><code>?</code><code>end</code><code> </code><code>t = 1 : time;</code><code>plot(t, x_true, 'b', t , x_hat, 'g', t, x_hat1, 'r'); </code><code>xlabel('time step'); ylabel('state');</code> <code>legend('True state', 'PF', 'PSO_PF');</code><code>?</code><code>xhatPartRMS = sqrt((norm(x_true - x_hat ))^2 / time);</code><code>disp(['PF RMS error = ', num2str(xhatPartRMS)]); </code><code> </code><code>xhatPartRMS1 = sqrt((norm(x_true - x_hat1))^2 / time);</code><code>disp(['PSO_PF RMS error = ', num2str(xhatPartRMS1)]); </code><code> </code><code>?</code><code>?
Operation results
References
[1] Zhang Hong. Research on quantitative detection of immunochromatography test strips based on deep learning and particle swarm optimization algorithm [D]. Xiamen University, 2018.
[2] Chen Zhimin, Bo Yuming, Wu Panlong, et al. A new adaptive particle swarm optimization particle filter algorithm and its application [J]. Journal of Applied Science, 2013, 31(3):285-293.DOI:10.3969/j.issn .0255-8297.2013.03.011.
[3] Hu Jianyang, Duan Xianhua, Ma Qixing. Sonar target tracking before detection based on particle swarm optimization particle filtering [J]. Ship Engineering, 2022(001):044.
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1 Improvements and applications of various intelligent optimization algorithms
Production scheduling, economic scheduling, assembly line scheduling, charging optimization, workshop scheduling, departure optimization, reservoir scheduling, three-dimensional packing, logistics location selection, cargo space optimization, bus scheduling optimization, charging pile layout optimization, workshop layout optimization, Container ship stowage optimization, water pump combination optimization, medical resource allocation optimization, facility layout optimization, visible area base station and drone site selection optimization
2 Machine learning and deep learning
Convolutional neural network (CNN), LSTM, support vector machine (SVM), least squares support vector machine (LSSVM), extreme learning machine (ELM), kernel extreme learning machine (KELM), BP, RBF, width Learning, DBN, RF, RBF, DELM, XGBOOST, TCN realize wind power prediction, photovoltaic prediction, battery life prediction, radiation source identification, traffic flow prediction, load prediction, stock price prediction, PM2.5 concentration prediction, battery health status prediction, water body Optical parameter inversion, NLOS signal identification, accurate subway parking prediction, transformer fault diagnosis
2. Image processing
Image recognition, image segmentation, image detection, image hiding, image registration, image splicing, image fusion, image enhancement, image compressed sensing
3 Path planning
Traveling salesman problem (TSP), vehicle routing problem (VRP, MVRP, CVRP, VRPTW, etc.), UAV three-dimensional path planning, UAV collaboration, UAV formation, robot path planning, raster map path planning , multimodal transportation problems, vehicle collaborative UAV path planning, antenna linear array distribution optimization, workshop layout optimization
4 UAV applications
UAV path planning, UAV control, UAV formation, UAV collaboration, UAV task allocation, and online optimization of UAV safe communication trajectories
5 Wireless sensor positioning and layout
Sensor deployment optimization, communication protocol optimization, routing optimization, target positioning optimization, Dv-Hop positioning optimization, Leach protocol optimization, WSN coverage optimization, multicast optimization, RSSI positioning optimization
6 Signal processing
Signal recognition, signal encryption, signal denoising, signal enhancement, radar signal processing, signal watermark embedding and extraction, EMG signal, EEG signal, signal timing optimization
7 Power system aspects
Microgrid optimization, reactive power optimization, distribution network reconstruction, energy storage configuration
8 Cellular Automata
Traffic flow, crowd evacuation, virus spread, crystal growth
9 Radar aspect
Kalman filter tracking, track correlation, track fusion
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