Direct symbol decoding using GA-SVM in chaotic baseband wireless communication system

Yin, Hui-Ping; Ren, Hang

doi:10.1016/j.jfranklin.2021.06.012

Cited by 13 publications

(5 citation statements)

References 40 publications

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“…This mapping is nonlinear and is implemented using kernel functions. Within this method it is difficult to choose the right kernel function [ 39 , 40 ].…”

Section: Methodology For Identifying the Types Of Defects Of Bldc Motorsmentioning

confidence: 99%

Finding fault types of BLDC motors within UAVs using machine learning techniques

Andrioaia,

Gaitan

2024

Heliyon

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“…This mapping is nonlinear and is implemented using kernel functions. Within this method it is difficult to choose the right kernel function [ 39 , 40 ].…”

Section: Methodology For Identifying the Types Of Defects Of Bldc Motorsmentioning

confidence: 99%

Finding fault types of BLDC motors within UAVs using machine learning techniques

Andrioaia,

Gaitan

2024

Heliyon

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“…Inspired by the biological natural selection and genetic mechanism, John Holland proposed the genetic algorithm in 1975 [45][46][47][48]. The GA starts with a set of randomly generated locations.…”

Section: Svm Optimized By Multiple Heuristic Algorithmsmentioning

confidence: 99%

“…Evolution is carried out in genotype iteration, and the best phenotype is selected in the environment according to the corresponding solution to solve the problem. The basic parameter setting in this algorithm is as follows: (i) GA Inspired by the biological natural selection and genetic mechanism, John Holland proposed the genetic algorithm in 1975 [45][46][47][48]. The GA starts with a set of randomly generated locations.…”

Section: Svm Optimized By Multiple Heuristic Algorithmsmentioning

confidence: 99%

Predicting Traffic Casualties Using Support Vector Machines with Heuristic Algorithms: A Study Based on Collision Data of Urban Roads

Zhong

2023

Sustainability

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Traffic accidents on urban roads are a major cause of death despite the development of traffic safety measures. However, the prediction of casualties in urban road traffic accidents has not been deeply explored in previous research. Effective forecasting methods for the casualties of traffic accidents can improve the manner of traffic accident warnings, further avoiding unnecessary loss. This paper provides a practicable model for traffic forecast problems, in which ten variables, including time characteristics, weather factors, accident types, collision characteristics, and road environment conditions, were selected as independent factors. A mixed-support vector machine (SVM) with a genetic algorithm (GA), sparrow search algorithm (SSA), grey wolf optimizer algorithm (GWO) and particle swarm optimization algorithm (PSO) separately are proposed to predict the casualties of collisions. Grounded on 4285 valid urban road traffic collisions, the computing results show that the SSA-SVM performs effectively in the casualties forecast compared with the GWO-SVM, GA-SVM and PSO-SVM.

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“…Yin et al proposed a symbol detection method based on GA-SVM to deal with this problem from different angles and transformed the symbol decoding process into a numerical calculation process. e results show that compared with the traditional method of calculating threshold decoding symbols, GA-SVM improves the bit error rate (BER) performance of CBWCS, simplifies the symbol detection process, and eliminates the process of channel identification and threshold calculation [12]. Zhaia et al proposed a hybrid method combining genetic algorithm (GA) and support vector machine (SVM).…”

Section: Related Workmentioning

confidence: 99%

“…In (12), K(x, x ′ ) represents the radial basis kernel function, x and x ′ are two training points, and σ represents the kernel function, that is, the width in the function direction, the mean square deviation of the Gaussian function, and the σ value directly proportional to the function width.…”

Section: Intelligent Financial Information Processing Model Based On ...mentioning

confidence: 99%

Construction of Enterprise Financial Information Intelligent Processing Innovation Model Based on Internet of Things Technology

Ding

2022

Computational Intelligence and Neuroscience

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The application of Internet of Things technology provides conditions for the systematization of enterprise financial data and the intellectualization of financial management. In this study, support vector machine (SVM) algorithm and genetic algorithm (GA) are combined to obtain the innovation model of enterprise financial information intelligent processing based on GA-SVM optimization algorithm. Nine factors affecting enterprise financial information processing from 2010 to 2018 are selected as influencing factors, and according to the idea and method of data modeling, the simulation experiment of enterprise financial information intelligent processing model is carried out with MATLAB. The results show that the average fitness of GA-SVM is close to the best fitness, indicating that each individual in the population is near the optimal solution. The RMSE of GA-SVM is 0.35%, and the root mean square error obtained is greater than this value, which shows that the application effect of GA-SVM is significantly better than that of SVM using cross validation method, and it is more suitable for precision prediction. At the same time, it also reflects that the optimization of SVM parameters by GA is reliable. The algorithm can be better applied in the intelligent processing of enterprise financial information and can provide a certain reference for enterprises in the financial management based on the Internet of Things.

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Direct symbol decoding using GA-SVM in chaotic baseband wireless communication system

Cited by 13 publications

References 40 publications

Finding fault types of BLDC motors within UAVs using machine learning techniques

Finding fault types of BLDC motors within UAVs using machine learning techniques

Predicting Traffic Casualties Using Support Vector Machines with Heuristic Algorithms: A Study Based on Collision Data of Urban Roads

Construction of Enterprise Financial Information Intelligent Processing Innovation Model Based on Internet of Things Technology

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