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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 machine learning, long short-term memory neural network (LSTM) is a powerful tool that is widely used in sequence data processing. However, the performance of LSTM networks still suffers from some limitations, such as slow convergence and low prediction accuracy. To solve these problems, researchers have been looking for ways to optimize LSTM networks. Recently, an optimization algorithm called the Golden Jackal algorithm was proposed and successfully applied to LSTM networks. This article will introduce the Adaboost classification prediction method based on the Golden Jackal algorithm to optimize the LSTM network, and attach the corresponding MATLAB code.
First, let us briefly review the basic principles of LSTM networks. The LSTM network is a type of recurrent neural network that better handles long-term dependencies by using gated units to remember and forget information. It consists of input gate, forget gate and output gate, which control the flow of information and memory. However, due to the complexity of the LSTM network, its training process is usually slow and easy to fall into local optimal solutions.
In order to optimize the LSTM network, the golden jackal algorithm was introduced. The golden jackal algorithm is a heuristic optimization algorithm based on the behavior of golden jackals in nature. It searches for the optimal solution by simulating the foraging behavior of golden jackals. It has global search capabilities and strong convergence, and can effectively optimize complex nonlinear problems. Combining the Golden Jackal algorithm with the LSTM network can improve the convergence speed and prediction accuracy of the network.
Next, we will introduce the Adaboost classification prediction method based on the Golden Jackal algorithm to optimize the LSTM network. Adaboost is an ensemble learning algorithm that builds a stronger classifier by combining multiple weak classifiers. In this method, we first use the golden jackal algorithm to train the LSTM network and obtain a set of optimized weight parameters. Then, these parameters are applied to the weak classifier in the Adaboost algorithm to get a more accurate classifier.
The following is the process of the GJO-LSTM-Adaboost algorithm:
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Initialize the weight parameters of the LSTM network and the weak classifier of the Adaboost algorithm.
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Use the golden jackal algorithm to train the LSTM network and obtain a set of optimized weight parameters.
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Apply optimized weight parameters to weak classifiers in Adaboost algorithm.
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Calculate the accuracy and error of Adaboost classifier.
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If the accuracy meets the requirements, end the algorithm; otherwise, return to step 2 to continue training and optimization.
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Output the final Adaboost classifier.
By optimizing the LSTM network using the Golden Jackal algorithm, we can significantly improve the accuracy and efficiency of classification predictions. In addition, by combining the Adaboost algorithm, we can further enhance the performance of the classifier.
We can see how to optimize the LSTM network using the Golden Jackal algorithm and apply it to the Adaboost algorithm. This combined approach can significantly improve the accuracy of classification predictions and speed up the convergence of the network.
To sum up, the Adaboost classification prediction method based on the Golden Jackal algorithm to optimize the LSTM network is a powerful machine learning technology. It combines the global search capability of the Golden Jackal algorithm and the sequence data processing capability of the LSTM network, and can effectively solve classification prediction problems. By using MATLAB code examples, we can better understand the implementation process of this method. I hope this article can provide some valuable reference for scholars and engineers who are studying and applying LSTM networks.
Part of the code
%% Clear environment variables</code><code>warning off % Close alarm information</code><code>close all % Close open figure window</code><code>clear % Clear variables</code><code>clc % clear command line</code><code>?</code><code>%% import data</code><code>res = xlsread('dataset.xlsx');</code><code>?</code><code>%% divide the training set and test set</code><code>temp = randperm(357);</code><code>?</code><code>P_train = res(temp(1: 240), 1: 12)';</code><code>T_train = res(temp(1: 240), 13)';</code><code>M = size(P_train, 2);</code><code>?</code><code>P_test = res(temp(241: end), 1: 12)';</code><code>T_test = res(temp(241: end), 13)';</code><code>N = size(P_test, 2);</code><code>?</code><code>%% data normalization化</code><code>[p_train, ps_input] = mapminmax(P_train, 0, 1);</code><code>p_test = mapminmax('apply', P_test, ps_input);</code><code>t_train = ind2vec(T_train);</code><code>t_test = ind2vec(T_test );
Operation results
References
[1] Li Da, Zhang Zhaosheng, Liu Peng, et al. Multi-weather vehicle classification method based on improved long short-term memory neural network-adaptive enhancement algorithm [J]. Automotive Engineering, 2020, 42(9):8.DOI:10.19562 /j.chinasae.qcgc.2020.09.015.
[2] Li Ruochen, Xiao Renbin. Prediction of public opinion evolution based on improved wolf pack algorithm to optimize LSTM network [J]. [2023-10-31].
[3] Xu Dongmei, Wang Yiyang, Wang Wenchuan. Annual runoff prediction of long short-term memory neural network model based on Bayesian optimization algorithm [J]. Hydropower Energy Science, 2022, 40(12):5.
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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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