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Author’s Note
✅ Author’s Introduction: A Matlab simulation developer passionate about research, improving both mindset and technology. For Matlab project collaboration, please DM or scan the QQ QR code in the article.
⛳️ Motto: A journey of a hundred miles begins with a single step.
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Matlab Neural Network Classification and Prediction (Advanced Version)
Introduction
📋📋📋 The contents of this article are as follows: 🎁🎁🎁
Contents
💥1 Overview
📚2 Code and Running Steps
🎉3 Running Results
🌈4 Matlab Version and References
🔎5 Code Acquisition Method
🍅6 Simulation Consultation
1 Overview
Intelligent optimization algorithms optimize the CNN-BiGRU-Attention wind power prediction
Intelligent optimization algorithms can be applied to optimize the parameters of the CNN-BiGRU-Attention wind power prediction model to improve the accuracy and effectiveness of predictions. Below are the principles and processes of CNN-BiGRU-Attention wind power prediction:
1 Principle:
– CNN (Convolutional Neural Network) is used to extract local features from time series data, constructed through convolutional and pooling layers, capturing the spatial relationships of the input data.
– BiGRU (Bidirectional Gated Recurrent Unit) is used to model the contextual information in time series data, constructed through forward and backward recurrent units, capturing the temporal relationships of the input data.
– The Attention mechanism is used to automatically learn important information from the input data by calculating the weights of each input element, enhancing the model’s focus on key information.
2 Process:
– Data Preprocessing: Preprocess the raw wind power data, including normalization and smoothing, to prepare it for training and prediction.
– Data Division: Divide the preprocessed data into training, validation, and test sets for model training and evaluation.
– Feature Extraction: Use the CNN model to extract features from the input time series data, generating local feature representations.
– Context Modeling: Use the BiGRU model to perform context modeling on the local feature representations, generating contextual feature representations of the time series.
– Attention Calculation: Use the Attention mechanism to perform attention calculations on the contextual feature representations, generating the most important feature representations.
– Prediction Output: Use a fully connected layer to predict the output from the feature representations, obtaining the final wind power prediction results.
– Model Training: Use optimization algorithms to optimize the model parameters, such as SGD, Adam, etc., by minimizing the loss function between the predicted results and actual values to update the model parameters.
– Model Evaluation: Use the validation set to evaluate the trained model, such as calculating the Mean Squared Error (MSE) and Mean Absolute Error (MAE) metrics of the prediction results.
– Prediction Application: Use the test set to predict with the trained model, obtaining the final wind power prediction results.
By optimizing the parameters of the CNN-BiGRU-Attention wind power prediction model through intelligent optimization algorithms, the model’s performance and accuracy can be improved, making the prediction results more accurate and reliable.
2 Code and Running Steps
(1) Directly run main.m to generate the graph with one click
3Running Results
3Running Results
4Matlab Version and References
1 Matlab Version
2019b or 2014a
2 References
[1] Fu Zongjian. Short-term Power Prediction of Photovoltaic Arrays Optimized by Genetic Algorithm Based BP Neural Network [J]. Electronic Devices. 2020
3 Note
This section is excerpted from the internet for reference only. If there is any infringement, please contact for deletion.
5 Code Acquisition Method
6 Simulation Consultation
**🍅 Simulation Consultation
1 Various Intelligent Optimization Algorithms Improvements and Applications**
1.1 PID Optimization
1.2 VMD Optimization
1.3 Distribution Network Restructuring
1.4 3D 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 Price 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 Absorption 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 Storage Location 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 Fully Connected Neural Network 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, Clear Water Value Prediction, Unemployment Rate Prediction, Electricity Consumption Prediction, Transportation 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 Recognition
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 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 Station + Time Window (ETWVRP)
4.2.3 Vehicle Routing Problem with Multiple Capacities (MCVRP)
4.2.4 Distance Multiple Vehicle Routing Problem (MDVRP)
4.2.5 Vehicle Routing Problem with Simultaneous Pickup and Delivery (SDVRP)
4.2.6 Vehicle Routing Problem with Time Window + Capacity (TWCVRP)
4.2.6 Vehicle Routing Problem with Time Window (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 Station
4.5.6 Chain Supermarket Delivery Path Planning
4.5.7 Vehicle Cooperative 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 Cooperative 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 Break 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 Radiation Pattern
7.2.17 Radar Clutter Simulation
**7.3 Biological Electrical Signals**
7.3.1 Electromyogram EMG
7.3.2 Electroencephalogram EEG
7.3.3 Electrocardiogram 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 Aspects**
7.6.1 WSN Localization
7.6.2 Height 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