NGO-CNN-SVM classification prediction | Matlab Northern Goshawk algorithm optimizes convolutional neural network-support vector machine classification prediction…

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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 today’s digital era, data classification has always been an important issue in the field of computer science. With the rise of big data and machine learning, researchers have been looking for more efficient ways to process and classify data. In this blog post, we will introduce a new data classification method based on Northern Goshawk Optimized Convolutional Neural Network combined with Support Vector Machine (NGO-CNN-SVM).

Convolutional neural network (CNN) is a widely used deep learning model that has achieved great success in fields such as image classification and pattern recognition. However, for some complex data classification problems, using CNN alone may not achieve the desired results. In order to improve the classification accuracy, we introduce support vector machine (SVM) as a post-processing method of CNN.

In the NGO-CNN-SVM method, we first optimize the CNN using the Northern Goshawk optimization algorithm. The northern goshawk optimization algorithm is a heuristic algorithm based on the foraging behavior of northern goshawks in nature. By simulating the foraging process of northern goshawks, this algorithm can effectively search for the optimal solution. We apply this algorithm to the training process of CNN to improve its performance and convergence speed.

Next, we use the optimized CNN to extract features of the data. CNN can effectively capture the local and global characteristics of the data through a series of convolution and pooling operations. These features are used as input to the support vector machine for final classification.

Support vector machine is a classic machine learning algorithm that performs well on binary and multi-classification problems. It separates data by finding an optimal hyperplane. In our method, we use support vector machines to classify features extracted by CNN to achieve more accurate data classification.

By combining the northern goshawk-optimized convolutional neural network and the support vector machine, our method is able to fully exploit the advantages of CNN in feature extraction and improve the classification accuracy through the classification ability of the support vector machine. In experiments, we use multiple datasets to evaluate our method. The results show that the NGO-CNN-SVM method achieves better classification accuracy than other methods on various data sets.

In short, the data classification method based on northern goshawk optimized convolutional neural network combined with support vector machine is an effective method that can improve the accuracy of data classification. With the continuous development of machine learning and deep learning, we believe that this method will play an important role in practical applications and provide new ideas and methods for solving other complex data classification problems.

Part of the code

% Gray Wolf Optimizer (GWO) source codes version 1.0 %
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% Developed in MATLAB R2011b(7.13) %
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% Author and programmer: Seyedali Mirjalili %
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% e-Mail: [email protected] %
% [email protected] %
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% Homepage: %
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% Main paper: S. Mirjalili, S. M. Mirjalili, A. Lewis %
% Gray Wolf Optimizer, Advances in Engineering %
% Software, in press, %
% DOI: 10.1016/j.advengsoft.2013.12.007 %
% %

% 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;

% If each variable has a different lb and ub
if Boundary_no>1
    for i=1:dim
        Positions(:,i)=rand(SearchAgents_no,1).*(ub_i-lb_i) + lb_i;

Running results

NGO-CNN-SVM classification prediction | Matlab Northern Goshawk algorithm optimizes convolutional neural Network-Support Vector Machine Classification Prediction_UAV

NGO-CNN-SVM classification prediction | Matlab Northern Goshawk algorithm optimizes convolutional neural Network-Support Vector Machine Classification Prediction_UAV_02

NGO-CNN-SVM classification prediction | Matlab Northern Goshawk algorithm optimizes convolutional neural Network-Support Vector Machine Classification Prediction_Data_03


[1] Zhang Dandan. Research on CNN classification model based on SVM and RF and its application in face detection [D]. Nanjing University of Posts and Telecommunications, 2016.

[2] Yang Hongyun, Huang Qiong, Sun Aizhen, et al. Rice seed image classification and recognition based on convolutional neural network and support vector machine [J]. Chinese Journal of Cereals and Oils, 2021(012):036.

[3] Wang Xiuxin, Yang Lulu, Tang Guyun, et al. Using convolutional neural network to extract karst forest information from high-resolution remote sensing images [J]. Science, Technology and Engineering, 2020.

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1 Improvement and application 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 application
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
Kalman filter tracking, track correlation, track fusion