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Financial management early warning model of enterprise circular economy based on chaotic particle swarm optimization algorithm

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JOURNAL OF INDUSTRIAL AND PRODUCTION ENGINEERING 2024年第3期41卷 217-228页

作者： Jiao, Jinlan Jiangsu Vocat Coll Finance & Econ Sch Accounting Huaian 223000 Peoples R China

Financial management is an important part of enterprise management, aiming to ensure the reasonable utilization of financial resources and help enterprises achieve business goals. This study utilizes chaotic particle swarm optimization algorithm to optimize the backpropagation neural network, and based on this, constructs a financial management early warning model for enterprise circular economy. Performance testing experiments show that the error reaches the target accuracy of 0.042881, with an R-value of 0.92287, indicating high prediction accuracy of the model;In the comparison experiment with the backpropagation model and the genetic feedback model, this study constructed a model with faster convergence speed and an overall accuracy of 92%, significantly higher than the other two models, further verifying the superiority of the chaotic particle swarm feedback neural network model. This indicates that the research model has certain application value in the financial management of circular economy in enterprises.

关键词： chaos algorithm enterprise cycle financial management particle swarm optimization early warning model

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Application of Improved chaos algorithm in the Identification of Rolling Motion of Trimaran

Application of Improved Chaos Algorithm in the Identificatio...

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2014年国际计算机科学与软件工程学术会议

作者： Zi-Fan WEI Sheng ZHANG Meng-Yun WANG Song-Lin YANG Jiangsu University of Science and Technology Zhenjiang Jiangsu China

In this paper,a high-speed slender composite trimaran is selected as the research *** the structural characteristics of the trimaran and the flow field around,the design variables,constraints and objective function have been selected to construct five kinds of optimization *** algorithm combined with parallel and hierarchical strategies has been applied in the design of the identification software system for rolling motion of ***,some rolling motion equations have been obtained by using the identification software with different *** the results from true experiment and identification software,the best rolling motion equation can be obtained,which has verified the reliability of the identification *** the other hand,a better algorithm for the identification system has been designed,which provides a basis for the further study on the *** shows that the appropriate number of parallel calculation and carrier calculation,the properly selecting the design variables used for parallel calculation and the appropriate carrier probability will help to increase the identification accuracy.

关键词： Trimaran Mathematical model Identification system chaos algorithm

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Optimized least-squares support vector machine for predicting aero-optic imaging deviation based on chaotic particle swarm optimization

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OPTIK 2020年 206卷 163215-000页

作者： Wu, Yuyang Xue, Wei Xu, Liang Guo, Xiang Xue, Deting Yao, Yuan Zhao, Songbo Li, Ningning Tianjin Univ Technol Tianjin Key Lab Control Theory & Applicat Complic Tianjin 300384 Peoples R China Tianjin Univ Technol Sch Elect & Elect Engn Tianjin 300384 Peoples R China Beijing Aerosp Prop Inst Beijing 100076 Peoples R China PICC Property & Casualty Co Ltd Big Data Ctr Beijing 100022 Peoples R China

In the time-averaged flow field, aero-optic imaging deviation (AOID) and its influencing variables are connected by complicated and strongly coupled nonlinear time-dependent relationships that present many difficulties for fast calculation of AOID. To achieve real-time online AOID compensation in practice, chaotic particle swarm optimization (CPSO) and the least-squares support vector machine (LSSVM) are combined to construct a predictive AOID model for supersonic aircraft in flight. With its strong global-search capability, the CPSO algorithm is used to optimize the parameters of the LSSVM predictive model. First, a chaotic sequence is used to initialize the particle positions, thereby enhancing search diversity. The premature convergence of normal particle swarm optimization (PSO) is then countered by using CPSO. If PSO falls into a local optimum, the extreme position of the population is adjusted and the current search trajectory of the particles is disturbed so that the particles search new neighborhoods and paths, thereby increasing the probability of escaping the local optimum. The simulation results show that the predictive accuracy of the CPSO-LSSVM model exceeds that of the LSSVM model alone or the back-propagation neural network model. The CPSO-LSSVM model is effective at compensating for AOID within a limited range, and it avoids the disadvantages of traditional geometrical-optics calculations, namely that they are time-consuming, laborious, and error-prone. When the training data of LSSVM is enough, any AOID can be estimated by the CPSO-LSSVM model.

关键词： Aero-optical effects Image deviation chaos algorithm Particle swarm algorithm LSSVM

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Prediction and Analysis of Literature Loan Circulation in University Libraries Based on RBF Neural Network Optimized Model

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AUTOMATIC CONTROL AND COMPUTER SCIENCES 2020年第2期54卷 139-146页

作者： Chen, Xia Yang, Wanqin Shangrao Normal Univ Lib Shangrao 334001 Jiangxi Peoples R China Xinyu Univ Xinyu 338004 Jiangxi Peoples R China

The circulation of library literature can reflect the utilization of literature, which is of great significance to the prediction. In this study, the Radial Basis Function (RBF) neural network was optimized by Grey Wolf Optimizer (GWO), and the optimization effect of GWO was improved by tent chaotic sequence. The improved GWO-RBF (IGWO-RBF) optimization model was obtained and applied to the prediction of library literature circulation. The experimental results showed that the IGWO-RBF could accurately predict the circulation of literature, with very small prediction error. The RBF prediction curve had the highest fitting degree with the actual curve, followed by the GWO-RBF model and IGWO-RBF model, and both the root-mean-square error (RMSE) and mean absolute percentage error (MAPE) of IGWO were small, indicating good prediction accuracy. This study proves the effectiveness of the RBF optimized model in the prediction of library circulation, and it can be popularized and applied in practice.

关键词： library circulation radial basis function neural network prediction grey wolf chaos algorithm

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K-Means Clustering algorithm Based on Chaotic Adaptive Artificial Bee Colony

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algorithmS 2021年第2期14卷 53页

作者： Jin, Qibing Lin, Nan Zhang, Yuming Beijing Univ Chem Technol Sch Informat Sci & Technol Beijing 100029 Peoples R China

K-Means Clustering is a popular technique in data analysis and data mining. To remedy the defects of relying on the initialization and converging towards the local minimum in the K-Means Clustering (KMC) algorithm, a chaotic adaptive artificial bee colony algorithm (CAABC) clustering algorithm is presented to optimally partition objects into K clusters in this study. This algorithm adopts the max-min distance product method for initialization. In addition, a new fitness function is adapted to the KMC algorithm. This paper also reports that the iteration abides by the adaptive search strategy, and Fuch chaotic disturbance is added to avoid converging on local optimum. The step length decreases linearly during the iteration. In order to overcome the shortcomings of the classic ABC algorithm, the simulated annealing criterion is introduced to the CAABC. Finally, the confluent algorithm is compared with other stochastic heuristic algorithms on the 20 standard test functions and 11 datasets. The results demonstrate that improvements in CAABA-K-means have an advantage on speed and accuracy of convergence over some conventional algorithms for solving clustering problems.

关键词： artificial bee colony (ABC) algorithm K-means clustering (KMC) algorithm chaos algorithm Metropolis algorithm simulated annealing

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Image Encryption and Decryption Based on Chaotic algorithm

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Journal of Applied Mathematics and Physics 2020年第9期8卷 1814-1825页

作者： Yue Hu Ruyue Tian School of Mathematics and Physics China University of Geosciences Wuhan China

This paper analyzes the problems in image encryption and decryption based on chaos theory. This article introduces the application of the two-stage Logistic algorithm in image encryption and decryption, then by information entropy analysis it is concluded that the security of this algorithm is higher compared with the original image;And a new image encryption and decryption algorithm based on the combination of two-stage Logistic mapping and M sequence is proposed. This new algorithm is very sensitive to keys;the key space is large and its security is higher than two-stage Logistic mapping of image encryption and decryption technology.

关键词： chaos algorithm Image Encryption and Decryption Two-Stage Logistic Mapping M Sequence

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Gesture Recognition Based on BP Neural Network Improved by Chaotic Genetic algorithm

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International Journal of Automation and computing 2018年第3期15卷 267-276页

作者： Dong-Jie Li Yang-Yang Li Jun-Xiang Li Yu Fu School of Automation Harbin University of Science and Technology

Aim at the defects of easy to fall into the local minimum point and the low convergence speed of back propagation（BP）neural network in the gesture recognition, a new method that combines the chaos algorithm with the genetic algorithm（CGA） is proposed. According to the ergodicity of chaos algorithm and global convergence of genetic algorithm, the basic idea of this paper is to encode the weights and thresholds of BP neural network and obtain a general optimal solution with genetic algorithm, and then the general optimal solution is optimized to the accurate optimal solution by adding chaotic disturbance. The optimal results of the chaotic genetic algorithm are used as the initial weights and thresholds of the BP neural network to recognize the gesture. Simulation and experimental results show that the real-time performance and accuracy of the gesture recognition are greatly improved with CGA.

关键词： Gesture recognition back propagation （BP） neural network chaos algorithm genetic algorithm data glove.

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Research on Fractional Order Control of an Improved Particle Swarm Optimization algorithm

Research on Fractional Order Control of an Improved Particle...

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Chinese Automation Congress (CAC)

作者： Lan, Xin Wei, Hongli Chen, Chaobo Gao, Song Xian Technol Univ Coll Elect & Informat Engn Xian Shaanxi Peoples R China Xian Technol Univ Xian Shaanxi Peoples R China

ISBN: (纸本)9781728113128

This is a fractional order control algorithm basing on improved particle swarm optimization (chaotic adaptive particle swarm optimization (CAPSO)), which is verified by the single inverted pendulum control system. This method combines the chaos algorithm with inertia weight adjustment of the particle swarm algorithm. And after the chaotic particle swarm initialization, the method exerts chaotic search on the swarms falling into the local optimal particle, namely the optimization of nonlinear inertia weight adjustment method does not only improve the convergence accuracy of the algorithm, but get the global optimum solution. The Experimental results show that the CAPSO algorithm is better than the dominant pole method and the particle swarm optimization algorithm (PSO) in the parameter tuning of the fractional order *** with PSO algorithm, it has many merits of the fast convergence speed, small overshoot, good stability and strong anti-interference. the Dynamic response of the system with fractional order controller optimized by CAPSO algorithm is better than that with integerorder PM controller.

关键词： Fractionalorder chaos algorithm Particle swarm optimization Inertia weigh

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A Novel Encryption Scheme for Digital Images based on Compound chaos algorithm

A Novel Encryption Scheme for Digital Images based on Compou...

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International Conference on Advanced Materials and Computer Science

作者： Han Fengying Zhu Congxu Liu Changsheng Changsha Aeronaut Vocat & Tech Coll Changsha 410014 Hunan Peoples R China South Cent Univ Technol Informat Sci & Engn Coll Changsha 410083 Peoples R China

ISBN: (纸本)9783037850978

A compound chaos encryption algorithm for digital images is designed in this paper. Lorenz Chaotic system and Logistics map are linked in cascade to generate chaos sequences. Then the permutation matrix is constructed to encrypt the digital image. The new algorithm can expand the key space and improve anti-aggressive ability. Matlab7.0 is used for simulation testing of the image encryption. Experimental results show that the algorithm is feasible and it can satisfy the security requirement for image transmission and storage. The compound chaos algorithm can be also used for color digital image encryption.

关键词： Encryption Scheme Digital Image chaos algorithm

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Comparative Study of Several Improved Firefly algorithms

Comparative Study of Several Improved Firefly Algorithms

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IEEE International Conference on Information and Automation (ICIA)

作者： Ma, Liangyu Cao, Pengrui North China Elect Power Univ Dept Automat Baoding 071003 Hebei Peoples R China

ISBN: (纸本)9781509041022

Firefly algorithm (FA), first put forward by a Cambridge scholar Yang, is a kind of swarm intelligent algorithm imitating nature fireflies' predation and courtship behaviour. It has been widely studied and gradually applied to different engineering fields. However, the basic FA has several shortcomings such as easiness to fall into local optimum, premature convergence, et al. Recent years have witnessed different ways taken by some scholars to improve the FA. In this work, two improved firefly algorithms, inertia weight firefly algorithm and chaos-based firefly algorithm, are deeply investigated. The two algorithms are simulated and their performances are compared through typical benchmark functions.

关键词： Firefly algorithm inertia weight chaos algorithm comparative study

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