Click the blue text above to follow us
🔊 Author Introduction: 985 Graduate, Research and Developmenter in the MATLAB Field;
🚅座右铭：行百里者半九十。
✅ Research Scope: Complete Code, Paper Reproduction, Program Customization, Journal Writing, Research Collaboration
🏆 Code Acquisition Method 1: Method to Obtain MATLAB Poseidon Code
🏆 Code Acquisition Method 2:
For more MATLAB optimization simulation content, click below👇
MATLAB Optimization (Advanced Version)
Introduction
📋📋📋 The table of contents is as follows: 🎁🎁🎁
Table of Contents
💥1 Overview
📚2 Partial Code and Running Steps
🎉3 Running Results
🌈4 MATLAB Version and References
🔎5 Code Acquisition Method
🍅6 Simulation Consultation
1 Overview
**1 Principle of PSO Algorithm in Characterizing Piezoelectric Material Parameters** Particle Swarm Optimization (PSO) optimizes the objective function by simulating collective intelligence behavior. In the characterization of piezoelectric material parameters, PSO is used to minimize the error between the measured impedance curve and the model simulation impedance curve. Each particle represents a set of candidate parameters (such as elastic constants, dielectric constants, piezoelectric coefficients), and iteratively updates the particle position (parameter combination) to approach the global optimal solution. The fitness function is typically defined as: $$ f(\theta) = \sum_{i=1}^{N} |Z_{meas}(f_i) – Z_{model}(f_i, \theta)|^2 $$ where $Z_{meas}$ is the measured impedance, $Z_{model}$ is the simulated impedance based on the equivalent circuit model, and $\theta$ is the parameter vector to be determined. **2 Impedance Measurement and Preprocessing Process** The impedance analyzer measures the impedance magnitude and phase of the piezoelectric device over a specific frequency range (usually 1Hz-10MHz). The raw data must undergo normalization and noise filtering to eliminate instrument errors and parasitic effects. Typical preprocessing includes: – Calibration to remove fixture parasitic parameters – Data interpolation to ensure uniform distribution of frequency points – Logarithmic transformation to enhance low-frequency weight **3 Equivalent Circuit Model Construction** The commonly used Butterworth-Van Dyke (BVD) equivalent circuit model for piezoelectric devices is: $$ Z_{model} = \left[ j\omega C_0 + \frac{1}{R_m + j\omega L_m + 1/(j\omega C_m)} \right]^{-1} $$ The parameter vector $\theta = [C_0, R_m, L_m, C_m]$ corresponds to static capacitance, mechanical loss, dynamic inductance, and dynamic capacitance, respectively. **4 PSO Parameter Extraction Implementation Steps** Initialize the particle swarm, setting the parameter ranges based on the material type: – $C_0$: 1pF-100nF – $R_m$: 0.1-100Ω – $L_m$: 1μH-1H – $C_m$: 1pF-1μF Set PSO hyperparameters: – Number of particles: 30-100 – Learning factors: $c_1=c_2=1.5$ – Inertia weight: 0.4-0.9 linearly decreasing – Maximum number of iterations: 200-500 In each iteration, calculate the particle fitness and update the individual and global optimal positions. The termination condition is a relative error less than 1e-4 or reaching the maximum number of iterations. **5 Result Verification and Error Analysis** After obtaining the optimal parameters, cross-validation is required: 1. Compare the correlation coefficient between the measured and simulated impedance curves (should be >0.95) 2. Check the physical validity of the parameters (e.g., negative values are invalid) 3. Verify transducer efficiency: $$ k_t = \frac{e_{33}}{\sqrt{c_{33}^D \epsilon_{33}^S}} $$ where $e_{33}$ is the piezoelectric constant, $c_{33}^D$ is the elastic stiffness, and $\epsilon_{33}^S$ is the dielectric constant. The typical $k_t$ for PZT materials should be in the range of 0.4-0.7. **6 Notes** The frequency range selection must cover resonance/anti-resonance peaks: – Thin sheet samples: 100kHz-10MHz – Bulk samples: 10kHz-1MHz For multilayer structures, a modified BVD model should be used, adding parallel branches. Anisotropic materials need to introduce direction-dependent parameters, which will expand the parameter vector dimension to 6-8.
2 Partial Code and Running Steps
2.1 Partial Code2.2 Running Steps
(1) Directly run main.m to generate the plot with one click
3Running Results
4MATLAB Version and References
1 MATLAB Version
2019b
2 References
[1] Hu Shaochuang. Research on Estimation of Thermal Radiation Parameters of Translucent Materials Based on Particle Swarm Algorithm[D]. Civil Aviation University of China. 2024
3 Remarks
This section is excerpted from the internet for reference only. If there is any infringement, please contact for removal.
5 Code Acquisition Method
6 Simulation Consultation
**🍅 Simulation Consultation
1 Various Intelligent Optimization Algorithm Improvements and Applications**
1.1 PID Optimization
1.2 VMD Optimization
1.3 Distribution Network Restructuring
1.4 Three-Dimensional Packing
1.5 Microgrid Optimization
1.6 Layout Optimization
1.7 Parameter Optimization
1.8 Cost Optimization
1.9 Charging Optimization
1.10 Scheduling Optimization
1.11 Pricing Optimization
1.12 Departure Optimization
1.13 Distribution Optimization
1.14 Coverage Optimization
1.15 Control Optimization
1.16 Inventory Optimization
1.17 Routing Optimization
1.18 Design Optimization
1.19 Location Optimization
1.20 Absorber Optimization
1.21 Site Selection Optimization
1.22 Operation Optimization
1.23 Assignment Optimization
1.24 Combination Optimization
1.25 Workshop Scheduling
1.26 Production Scheduling
1.27 Economic Scheduling
1.28 Assembly Line Scheduling
1.29 Reservoir Scheduling
1.30 Cargo Space Optimization
1.31 Bus Scheduling Optimization
1.32 Container Ship Loading Optimization
1.33 Pump Combination Optimization
1.34 Medical Resource Allocation Optimization
1.35 Visual Field Base Station and Drone Site Selection Optimization
**2 Machine Learning and Deep Learning Classification and Prediction**
**2.1 Machine Learning and Deep Learning Classification**
2.1.1 BiLSTM Bidirectional Long Short-Term Memory Neural Network Classification
2.1.2 BP Neural Network Classification
2.1.3 CNN Convolutional Neural Network Classification
2.1.4 DBN Deep Belief Network Classification
2.1.5 DELM Deep Learning Extreme Learning Machine Classification
2.1.6 ELMAN Recurrent Neural Network Classification
2.1.7 ELM Extreme Learning Machine Classification
2.1.8 GRNN General Regression Neural Network Classification
2.1.9 GRU Gated Recurrent Unit Classification
2.1.10 KELM Hybrid Kernel Extreme Learning Machine Classification
2.1.11 KNN Classification
2.1.12 LSSVM Least Squares Support Vector Machine Classification
2.1.13 LSTM Long Short-Term Memory Network Classification
2.1.14 MLP Multi-Layer Perceptron Classification
2.1.15 PNN Probabilistic Neural Network Classification
2.1.16 RELM Robust Extreme Learning Machine Classification
2.1.17 RF Random Forest Classification
2.1.18 SCN Stochastic Configuration Network Model Classification
2.1.19 SVM Support Vector Machine Classification
2.1.20 XGBOOST Classification
**2.2 Machine Learning and Deep Learning Prediction**
2.2.1 ANFIS Adaptive Neuro-Fuzzy Inference System Prediction
2.2.2 ANN Artificial Neural Network Prediction
2.2.3 ARMA Autoregressive Moving Average Model Prediction
2.2.4 BF Particle Filter Prediction
2.2.5 BiLSTM Bidirectional Long Short-Term Memory Neural Network Prediction
2.2.6 BLS Broad Learning System Prediction
2.2.7 BP Neural Network Prediction
2.2.8 CNN Convolutional Neural Network Prediction
2.2.9 DBN Deep Belief Network Prediction
2.2.10 DELM Deep Learning Extreme Learning Machine Prediction
2.2.11 DKELM Regression Prediction
2.2.12 ELMAN Recurrent Neural Network Prediction
2.2.13 ELM Extreme Learning Machine Prediction
2.2.14 ESN Echo State Network Prediction
2.2.15 FNN Feedforward Neural Network Prediction
2.2.16 GMDN Prediction
2.2.17 GMM Gaussian Mixture Model Prediction
2.2.18 GRNN General Regression Neural Network Prediction
2.2.19 GRU Gated Recurrent Unit Prediction
2.2.20 KELM Hybrid Kernel Extreme Learning Machine Prediction
2.2.21 LMS Least Mean Squares Algorithm Prediction
2.2.22 LSSVM Least Squares Support Vector Machine Prediction
2.2.23 LSTM Long Short-Term Memory Network Prediction
2.2.24 RBF Radial Basis Function Neural Network Prediction
2.2.25 RELM Robust Extreme Learning Machine Prediction
2.2.26 RF Random Forest Prediction
2.2.27 RNN Recurrent Neural Network Prediction
2.2.28 RVM Relevance Vector Machine Prediction
2.2.29 SVM Support Vector Machine Prediction
2.2.30 TCN Temporal Convolutional Network Prediction
2.2.31 XGBoost Regression Prediction
2.2.32 Fuzzy Prediction
2.2.33 Singular Spectrum Analysis Method SSA Time Series Prediction
**2.3 Machine Learning and Deep Learning Practical Application Prediction**
CPI Index Prediction, PM2.5 Concentration Prediction, SOC Prediction, Financial Warning Prediction, Yield Prediction, Parking Space Prediction, Pest Prediction, Steel Thickness Prediction, Battery Health Status Prediction, Power Load Prediction, Housing Price Prediction, Corrosion Rate Prediction, Fault Diagnosis Prediction, Photovoltaic Power Prediction, Trajectory Prediction, Aircraft Engine Life Prediction, Exchange Rate Prediction, Concrete Strength Prediction, Heating Furnace Temperature Prediction, Price Prediction, Traffic Flow Prediction, Resident Consumption Index Prediction, Air Quality Prediction, Grain Temperature Prediction, Temperature Prediction, Clean Water Value Prediction, Unemployment Rate Prediction, Electricity Consumption Prediction, Transport Volume Prediction, Manufacturing Purchasing Managers Index Prediction
**3 Image Processing Aspects**
**3.1 Image Edge Detection**
**3.2 Image Processing**
**3.3 Image Segmentation**
**3.4 Image Classification**
**3.5 Image Tracking**
**3.6 Image Encryption and Decryption**
**3.7 Image Retrieval**
**3.8 Image Registration**
**3.9 Image Stitching**
**3.10 Image Evaluation**
**3.11 Image Denoising**
**3.12 Image Fusion**
**3.13 Image Recognition**
3.13.1 Dial Recognition
3.13.2 Lane Line Recognition
3.13.3 Vehicle Counting
3.13.4 Vehicle Recognition
3.13.5 License Plate Recognition
3.13.6 Parking Space Recognition
3.13.7 Size Detection
3.13.8 Answer Sheet Recognition
3.13.9 Appliance Recognition
3.13.10 Fall Detection
3.13.11 Animal Recognition
3.13.12 QR Code Recognition
3.13.13 Invoice Recognition
3.13.14 Clothing Recognition
3.13.15 Chinese Character Recognition
3.13.16 Traffic Light Recognition
3.13.17 Iris Recognition
3.13.18 Fire Detection
3.13.19 Disease Classification
3.13.20 Traffic Sign Recognition
3.13.21 Card Number Recognition
3.13.22 Mask Recognition
3.13.23 Crack Detection
3.13.24 Target Tracking
3.13.25 Fatigue Detection
3.13.26 Flag Recognition
3.13.27 Grass Recognition
3.13.28 Face Recognition
3.13.29 Renminbi Recognition
3.13.30 ID Card Recognition
3.13.31 Gesture Recognition
3.13.32 Digit and Letter Recognition
3.13.33 Palm Recognition
3.13.34 Leaf Recognition
3.13.35 Fruit Recognition
3.13.36 Barcode Recognition
3.13.37 Temperature Detection
3.13.38 Defect Detection
3.13.39 Chip Detection
3.13.40 Behavior Recognition
3.13.41 Verification Code Recognition
3.13.42 Medicinal Material Recognition
3.13.43 Coin Recognition
3.13.44 Postal Code Recognition
3.13.45 Playing Card Recognition
3.13.46 Fingerprint Recognition
**3.14 Image Restoration**
**3.15 Image Compression**
**3.16 Image Steganography**
**3.17 Image Enhancement**
**3.18 Image Reconstruction**
**4 Path Planning Aspects**
**4.1 Traveling Salesman Problem (TSP)**
4.1.1 Single Traveling Salesman Problem (TSP)
4.1.2 Multiple Traveling Salesman Problem (MTSP)
**4.2 Vehicle Routing Problem (VRP)**
4.2.1 Vehicle Routing Problem (VRP)
4.2.2 Capacitated Vehicle Routing Problem (CVRP)
4.2.3 Capacitated + Time Window + Distance Vehicle Routing Problem (DCTWVRP)
4.2.4 Capacitated + Distance Vehicle Routing Problem (DCVRP)
4.2.5 Distance Vehicle Routing Problem (DVRP)
4.2.6 Vehicle Routing Problem with Charging Stations + Time Windows (ETWVRP)
4.2.3 Vehicle Routing Problem with Multiple Capacities (MCVRP)
4.2.4 Distance Multi-Vehicle Routing Problem (MDVRP)
4.2.5 Simultaneous Pickup and Delivery Vehicle Routing Problem (SDVRP)
4.2.6 Vehicle Routing Problem with Time Windows + Capacities (TWCVRP)
4.2.6 Vehicle Routing Problem with Time Windows (TWVRP)
**4.3 Multimodal Transport Problem**
**4.4 Robot Path Planning**
4.4.1 Obstacle Avoidance Path Planning
4.4.2 Maze Path Planning
4.4.3 Grid Map Path Planning
**4.5 Delivery Path Planning**
4.5.1 Cold Chain Delivery Path Planning
4.5.2 Takeout Delivery Path Planning
4.5.3 Mask Delivery Path Planning
4.5.4 Medicine Delivery Path Planning
4.5.5 Delivery Path Planning with Charging Stations
4.5.6 Supermarket Delivery Path Planning
4.5.7 Vehicle Collaborative Drone Delivery Path Planning
**4.6 Drone Path Planning**
4.6.1 Aircraft Simulation
4.6.2 Drone Flight Operations
4.6.3 Drone Trajectory Tracking
4.6.4 Drone Swarm Simulation
4.6.5 Drone 3D Path Planning
4.6.6 Drone Formation
4.6.7 Drone Collaborative Tasks
4.6.8 Drone Task Assignment
**5 Speech Processing**
**5.1 Speech Emotion Recognition**
**5.2 Sound Source Localization**
**5.3 Feature Extraction**
**5.4 Speech Coding**
**5.5 Speech Processing**
**5.6 Speech Separation**
**5.7 Speech Analysis**
**5.8 Speech Synthesis**
**5.9 Speech Encryption**
**5.10 Speech Denoising**
**5.11 Speech Recognition**
**5.12 Speech Compression**
**5.13 Speech Hiding**
**6 Cellular Automata Aspects**
**6.1 Cellular Automata Virus Simulation**
**6.2 Cellular Automata Urban Planning**
**6.3 Cellular Automata Traffic Flow**
**6.4 Cellular Automata Gas**
**6.5 Cellular Automata Personnel Evacuation**
**6.6 Cellular Automata Forest Fire**
**6.7 Cellular Automata Game of Life**
**7 Signal Processing Aspects**
**7.1 Fault Signal Diagnosis Analysis**
7.1.1 Gear Damage Identification
7.1.2 Asynchronous Motor Rotor Broken Bar Fault Diagnosis
7.1.3 Rolling Element Inner and Outer Ring Fault Diagnosis Analysis
7.1.4 Motor Fault Diagnosis Analysis
7.1.5 Bearing Fault Diagnosis Analysis
7.1.6 Gearbox Fault Diagnosis Analysis
7.1.7 Three-Phase Inverter Fault Diagnosis Analysis
7.1.8 Diesel Engine Fault Diagnosis
**7.2 Radar Communication**
7.2.1 FMCW Simulation
7.2.2 GPS Anti-Jamming
7.2.3 Radar LFM
7.2.4 Radar MIMO
7.2.5 Radar Angle Measurement
7.2.6 Radar Imaging
7.2.7 Radar Positioning
7.2.8 Radar Echo
7.2.9 Radar Detection
7.2.10 Radar Digital Signal Processing
7.2.11 Radar Communication
7.2.12 Radar Phased Array
7.2.13 Radar Signal Analysis
7.2.14 Radar Early Warning
7.2.15 Radar Pulse Compression
7.2.16 Antenna Direction Pattern
7.2.17 Radar Clutter Simulation
**7.3 Biological Electrical Signals**
7.3.1 Electromyography (EMG)
7.3.2 Electroencephalography (EEG)
7.3.3 Electrocardiography (ECG)
7.3.4 Heart Simulation
**7.4 Communication Systems**
7.4.1 DOA Estimation
7.4.2 LEACH Protocol
7.4.3 Encoding and Decoding
7.4.4 Variational Mode Decomposition
7.4.5 Ultra-Wideband Simulation
7.4.6 Multipath Fading Simulation
7.4.7 Cellular Networks
7.4.8 Pipeline Leakage
7.4.9 Empirical Mode Decomposition
7.4.10 Filter Design
7.4.11 Analog Signal Transmission
7.4.12 Analog Signal Modulation
7.4.13 Digital Baseband Signal
7.4.14 Digital Channel
7.4.15 Digital Signal Processing
7.4.16 Digital Signal Transmission
7.4.17 Digital Signal Denoising
7.4.18 Underwater Acoustic Communication
7.4.19 Communication Simulation
7.4.20 Wireless Transmission
7.4.21 Bit Error Rate Simulation
7.4.22 Modern Communication
7.4.23 Channel Estimation
7.4.24 Signal Detection
7.4.25 Signal Fusion
7.4.26 Signal Recognition
7.4.27 Compressed Sensing
7.4.28 Noise Simulation
7.4.29 Noise Interference
**7.5 Drone Communication**
**7.6 Wireless Sensor Localization and Layout**
7.6.1 WSN Localization
7.6.2 Altitude Estimation
7.6.3 Filtering and Tracking
7.6.4 Target Localization
7.6.4.1 Dv-Hop Localization
7.6.4.2 RSSI Localization
7.6.4.3 Intelligent Algorithm Optimization Localization
7.6.5 Integrated Navigation
**8 Power System Aspects**
Microgrid Optimization, Reactive Power Optimization, Distribution Network Restructuring, Energy Storage Configuration
