scholarly journals An Improved Particle Swarm Optimization-Powered Adaptive Classification and Migration Visualization for Music Style

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xiahan Liu
Keyword(s):  
Real Time ◽  
Recognition Rate ◽  
Particle Swarm ◽  
Learning Performance ◽  
Practical Significance ◽  
Particle Swarm Algorithm ◽  
Specific Content ◽  
Style Transfer ◽  
Music Visualization ◽  
And Migration

Based on the adaptive particle swarm algorithm and error backpropagation neural network, this paper proposes methods for different styles of music classification and migration visualization. This method has the advantages of simple structure, mature algorithm, and accurate optimization. It can find better network weights and thresholds so that particles can jump out of the local optimal solutions previously searched and search in a larger space. The global search uses the gradient method to accelerate the optimization and control the real-time generation effect of the music style transfer, thereby improving the learning performance and convergence performance of the entire network, ultimately improving the recognition rate of the entire system, and visualizing the musical perception. This kind of real-time information visualization is an artistic expression form, in which artificial intelligence imitates human synesthesia, and it is also a kind of performance art. Combining traditional music visualization and image style transfer adds specific content expression to music visualization and time sequence expression to image style transfer. This visual effect can help users generate unique and personalized portraits with music; it can also be widely used by artists to express the relationship between music and vision. The simulation results show that the method has better classification performance and has certain practical significance and reference value.

scholarly journals Parameters Optimization and Application of SVM Based on PCA-Particle Swarm Algorithm

2019 ◽  
pp. 325-330 ◽  
Author(s):  
Qingmi Yang
Keyword(s):  
Parameter Optimization ◽  
Recognition Accuracy ◽  
Recognition Rate ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Principal Component ◽  
Pso Algorithm ◽  
Parameters Optimization ◽  
Support Vector ◽  
Particle Swarm Algorithm

The parameter optimization of Support Vector Machine (SVM) has been a hot research direction. To improve the optimization rate and classification performance of SVM, the Principal Component Analysis (PCA) - Particle Swarm Optimization (PSO) algorithm was used to optimize the penalty parameters and kernel parameters of SVM. PSO which is to find the optimal solution through continuous iteration combined with PCA that eliminates linear redundancy between data, effectively enhance the generalization ability of the model, reduce the optimization time of parameters, and improve the recognition accuracy. The simulation comparison experiments on 6 UCI datasets illustrate that the excellent performance of the PCA-PSO-SVM model. The results show that the proposed algorithm has higher recognition accuracy and better recognition rate than simple PSO algorithm in the parameter optimization of SVM. It is an effective parameter optimization method.

scholarly journals Research on the Effect of DPSO in Team Selection Optimization under the Background of Big Data

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Qian Zhao ◽  
Lian-ying Zhang
Keyword(s):  
Big Data ◽  
Rapid Development ◽  
Particle Swarm ◽  
Practical Significance ◽  
Particle Swarm Algorithm ◽  
Enterprise Information ◽  
Strategic Objectives ◽  
Management Platform ◽  
Team Selection ◽  
Swarm Algorithm

Team selection optimization is the foundation of enterprise strategy realization; it is of great significance for maximizing the effectiveness of organizational decision-making. Thus, the study of team selection/team foundation has been a hot topic for a long time. With the rapid development of information technology, big data has become one of the significant technical means and played a key role in many researches. It is a frontier of team selection study by the means of combining big data with team selection, which has the great practical significance. Taking strategic equilibrium matching and dynamic gain as association constraints and maximizing revenue as the optimization goal, the Hadoop enterprise information management platform is constructed to discover the external environment, organizational culture, and strategic objectives of the enterprise and to discover the potential of the customer. And in order to promote the renewal of production and cooperation mode, a team selection optimization model based on DPSO is built. The simulation experiment method is used to qualitatively analyze the main parameters of the particle swarm optimization in this paper. By comparing the iterative results of genetic algorithm, ordinary particle swarm algorithm, and discrete particle swarm algorithm, it is found that the DPSO algorithm is effective and preferred in the study of team selection with the background of big data.

scholarly journals A particle swarm optimization approach using adaptive entropy-based fitness quantification of expert knowledge for high-level, real-time cognitive robotic control

2019 ◽  
Vol 1 (12) ◽  
Author(s):  
Deon de Jager ◽  
Yahya Zweiri ◽  
Dimitrios Makris
Keyword(s):  
Real Time ◽  
Flight Control ◽  
Autonomous Robots ◽  
Expert Knowledge ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Optimization Approach ◽  
Autonomous Flight ◽  
Challenge Control ◽  
High Level

AbstractHigh-level, real-time mission control of semi-autonomous robots, deployed in remote and dynamic environments, remains a challenge. Control models, learnt from a knowledgebase, quickly become obsolete when the environment or the knowledgebase changes. This research study introduces a cognitive reasoning process, to select the optimal action, using the most relevant knowledge from the knowledgebase, subject to observed evidence. The approach in this study introduces an adaptive entropy-based set-based particle swarm algorithm (AE-SPSO) and a novel, adaptive entropy-based fitness quantification (AEFQ) algorithm for evidence-based optimization of the knowledge. The performance of the AE-SPSO and AEFQ algorithms are experimentally evaluated with two unmanned aerial vehicle (UAV) benchmark missions: (1) relocating the UAV to a charging station and (2) collecting and delivering a package. Performance is measured by inspecting the success and completeness of the mission and the accuracy of autonomous flight control. The results show that the AE-SPSO/AEFQ approach successfully finds the optimal state-transition for each mission task and that autonomous flight control is successfully achieved.

scholarly journals Real-Time Application Optimization Control Algorithm for Energy Management Strategy of the Hybrid Power System Based on Artificial Intelligence

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yanying Ma ◽  
Qiang Liu
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Fuel Consumption ◽  
Energy Management ◽  
Management Strategy ◽  
Lithium Battery ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Energy Management Strategy ◽  
Hybrid Power

In recent years, due to the strengthening of our country’s comprehensive strength, the rapid development of science and technology and artificial intelligence has also attracted people’s attention. Artificial intelligence is a highly applicable subject, which has very good applications in power systems. In the experiment, the open circuit voltage method and the ampere-hour integration method are used to estimate the SOC of the lithium battery and the particle swarm energy management algorithm is used to allocate the output power of the fuel cell and the lithium battery. The particle swarm algorithm module calls the dual source hybrid power system module through the sim function to convert the actual value input in the system into a fuzzy quantity suitable for fuzzy control. The energy management strategy based on particle swarm optimization and fuzzy control was tested based on working conditions under the comprehensive test bench. Finally, the matching of the hybrid system is analyzed from the structure, component parameters, control strategy, and driving cycle of the vehicle. The experimental data show that the total fuel consumption of the three sets of experiments is averaged to get a fuel consumption rate of 26.3 m3/100 km for the hybrid city bus under the optimized energy management strategy. The results show that the real-time energy management strategy based on particle swarm algorithm can significantly improve the real-time performance of traditional instantaneous energy management strategies while reducing fuel consumption.

Application of Substructure Discovery Based PSO Algorithm in Distribution Network Economic Evaluation

2012 ◽  
Vol 433-440 ◽  
pp. 7054-7059
Author(s):  
Xiao Hui Zhang ◽  
Zhi Gang Lu
Keyword(s):  
Economic Evaluation ◽  
Distribution Network ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Evaluation Index System ◽  
Practical Significance ◽  
Particle Swarm Algorithm ◽  
Local Optimum ◽  
Algorithm Optimization ◽  
Search Capability

In this paper, a hierarchical economic evaluation index system for distribution network is established. The various indicators of the distribution network and the correlation between the distribution network in order to form is proposed. Substructure discovery algorithm is used to data mining. To avoid falling into local optimum, efficient global search capability of particle swarm is applied to substructure algorithm optimization. Finally, an example demonstrates the reasonablity and effectivity of the substructure found algorithm applied into distribution network economic evaluation. Optimization speed with particle swarm algorithm is fast. This study have practical significance to network planning.

GPU-Based PSO Application in Multiuser Detection and Trajectory Parameter Estimation

2013 ◽  
Vol 340 ◽  
pp. 829-832
Author(s):  
Lei Sun ◽  
Han Tao Zhang ◽  
Xiao Ping Zhou
Keyword(s):  
Parameter Estimation ◽  
Real Time ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
System Capacity ◽  
Particle Swarm Algorithm ◽  
Processing Unit ◽  
Trajectory Parameter ◽  
Multiuser Detector ◽  
Swarm Algorithm

The parallel character of particle swarm algorithm (PSO) and the Graphic Processing Unit (GPU) technology of Compute United Device Architecture (CUDA) from NVIDIA are analyzed. Two methods of the realization of PSO based on GPU are discussed. One method is using the module of open source particle swarm algorithm supporting the GPU, with the application of multiuser detector (MUD). The other method is using the module of MATLAB supporting the GPU with the application of the moving parameter estimation. The test results show that the PSO algorithm based on GPU technology can significantly improve the speed of system capacity, to solve the problem of multi-dimensional global optimization, with the poor real-time performance. It can be widely used in the project of high real-time requirements.

Research of a Mold Job Shop Scheduling Optimization Based on Particle Swarm Optimization Algorithm

2015 ◽  
Vol 757 ◽  
pp. 201-207
Author(s):  
Xiu Ju Lan ◽  
Dan Dan Su
Keyword(s):  
Particle Swarm Optimization ◽  
Production Management ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Particle Swarm ◽  
Practical Significance ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Shop Scheduling ◽  
Manufacturing Enterprises

Job shop scheduling is a key part of production management and control for manufacturing enterprises. An optimized scheduling is helpful for enterprise to strengthen its efficiency and competition. And particle swarm optimization is a young algorithm of swarm intelligence. So application and research of job shop scheduling based on particle swarm optimization has important practical significance. This paper analyze and diagnose the scheduling status of a mold manufacturing workshop, taking minimize make span and average of AI based on fuzzy processing-time and delivery as optimizing target, model the scheduling for the manufacturing of CQD-035. Eventually, programming on the platform of MATLAB7.0.1 using the discrete particle swarm algorithm, a satisfactory scheduling scheme is obtained.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

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