Control parameters of original differential evolution （DE） are kept fixed throughout the entire evolutionary process. However, it is not an easy task to properly set control parameters in DE for different optiinizat...Control parameters of original differential evolution （DE） are kept fixed throughout the entire evolutionary process. However, it is not an easy task to properly set control parameters in DE for different optiinization problems. According to the relative position of two different individual vectors selected to generate a difference vector in the searching place, a self-adapting strategy for the scale factor F of the difference vector is proposed. In terms of the convergence status of the target vector in the current population, a self-adapting crossover probability constant CR strategy is proposed. Therefore, good target vectors have a lower CFI while worse target vectors have a large CFI. At the same time, the mutation operator is modified to improve the convergence speed. The performance of these proposed approaches are studied with the use of some benchmark problems and applied to the trajectory planning of a three-joint redundant manipulator. Finally, the experiment results show that the proposed approaches can greatly improve robustness and convergence speed.展开更多
With the development of multimedia presentation technology,image acquisition technology and the Internet industry,long-distance communication methods have changed from the previous letter,the audio to the current audi...With the development of multimedia presentation technology,image acquisition technology and the Internet industry,long-distance communication methods have changed from the previous letter,the audio to the current audio/video.And the proportion of video in work,study and entertainment keeps increasing,high-definition video is getting more and more attention.Due to the limits of the network environment and storage capacity,the original video must be encoded to be efficiently transmitted and stored.High Efficient Video Coding(HEVC)requires a large amount of time to recursively traverse all possible quantization parameter values of the coding unit in the adaptive quantization process.The optimal quantization parameter is calculated by comparing the rate distortion cost.In this paper,we propose a fast decision method for HEVC quantization parameters selection based on convolutional neural network,which saves video’s encoding time.展开更多
A variable parameter self-adaptive control strategy based on driving condition identification is proposed to take full advantage of the fuel saving potential of the plug-in hybrid electric bus(PHEB).Firstly,the princi...A variable parameter self-adaptive control strategy based on driving condition identification is proposed to take full advantage of the fuel saving potential of the plug-in hybrid electric bus(PHEB).Firstly,the principal component analysis(PCA)and the fuzzy c-means clustering(FCM)algorithm is used to construct the comprehensive driving cycle,congestion driving cycle,urban driving cycle and suburban driving cycle of Chinese urban buses.Secondly,an improved particle swarm optimization(IPSO)algorithm is proposed,and is used to optimize the control parameters of PHEB under different driving cycles,respectively.Then,the variable parameter self-adaptive control strategy based on driving condition identification is given.Finally,for an actual running vehicle,the driving condition is identified by relevance vector machine(RVM),and the corresponding control parameters are selected to control the vehicle.The simulation results show that the fuel consumption of using the variable parameter self-adaptive control strategy is reduced by 4.2% compared with that of the fixed parameter control strategy,and the feasibility of the variable parameter self-adaptive control strategy is verified.展开更多
Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning paradigm.While this approach allows models to specialize in speci...Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning paradigm.While this approach allows models to specialize in specific tasks with reduced training costs,the substantial memory requirements during fine-tuning present a barrier to broader deployment.Parameter-Efficient Fine-Tuning(PEFT)techniques,such as Low-Rank Adaptation(LoRA),and parameter quantization methods have emerged as solutions to address these challenges by optimizing memory usage and computational efficiency.Among these,QLoRA,which combines PEFT and quantization,has demonstrated notable success in reducing memory footprints during fine-tuning,prompting the development of various QLoRA variants.Despite these advancements,the quantitative impact of key variables on the fine-tuning performance of quantized LLMs remains underexplored.This study presents a comprehensive analysis of these key variables,focusing on their influence across different layer types and depths within LLM architectures.Our investigation uncovers several critical findings:(1)Larger layers,such as MLP layers,can maintain performance despite reductions in adapter rank,while smaller layers,like self-attention layers,aremore sensitive to such changes;(2)The effectiveness of balancing factors depends more on specific values rather than layer type or depth;(3)In quantization-aware fine-tuning,larger layers can effectively utilize smaller adapters,whereas smaller layers struggle to do so.These insights suggest that layer type is a more significant determinant of fine-tuning success than layer depth when optimizing quantized LLMs.Moreover,for the same discount of trainable parameters,reducing the trainable parameters in a larger layer is more effective in preserving fine-tuning accuracy than in a smaller one.This study provides valuable guidance for more efficient fine-tuning strategies and opens avenues for further research into optimizing LLM fine-tuning in resource-constrained environments.展开更多
The high-efficiency video coder(HEVC)is one of the most advanced techniques used in growing real-time multimedia applications today.However,they require large bandwidth for transmission through bandwidth,and bandwidth...The high-efficiency video coder(HEVC)is one of the most advanced techniques used in growing real-time multimedia applications today.However,they require large bandwidth for transmission through bandwidth,and bandwidth varies with different video sequences/formats.This paper proposes an adaptive information-based variable quantization matrix(AIVQM)developed for different video formats having variable energy levels.The quantization method is adapted based on video sequence using statistical analysis,improving bit budget,quality and complexity reduction.Further,to have precise control over bit rate and quality,a multi-constraint prune algorithm is proposed in the second stage of the AI-VQM technique for pre-calculating K numbers of paths.The same should be handy to selfadapt and choose one of the K-path automatically in dynamically changing bandwidth availability as per requirement after extensive testing of the proposed algorithm in the multi-constraint environment for multiple paths and evaluating the performance based on peak signal to noise ratio(PSNR),bit-budget and time complexity for different videos a noticeable improvement in rate-distortion(RD)performance is achieved.Using the proposed AIVQM technique,more feasible and efficient video sequences are achieved with less loss in PSNR than the variable quantization method(VQM)algorithm with approximately a rise of 10%–20%based on different video sequences/formats.展开更多
Affine projection algorithm(APA)has been used to estimate the parameters of interior permanent magnet synchronous motor(IPMSM).However,there is not a strict guideline of choosing the stepsize of this algorithm to make...Affine projection algorithm(APA)has been used to estimate the parameters of interior permanent magnet synchronous motor(IPMSM).However,there is not a strict guideline of choosing the stepsize of this algorithm to make sure that the results of parameter estimation are convergent.In order to solve such problem,self-adaptive stepsize affine projection algorithm for parameter estimation of IPMSM is proposed in this paper.Compared with traditional affine projection algorithm,this method can obtain the stepsize automatically based on the operation condition,which can ensure the convergence and celerity of the process of parameter estimation.Then,on the basis of self-adaptive stepsize affine projection algorithm,a novel parameter estimation method based on square-wave current injection is proposed.By this method,the error of estimated parameter caused by stator resistance,linkage magnetic flux and dead-time voltage can be reduced effectively.Finally,the proposed parameter estimation method is verified by experiments on a 2.2-kW IPMSM drive platform.展开更多
This paper addresses a modified auxiliary model stochastic gradient recursive parameter identification algorithm(M-AM-SGRPIA)for a class of single input single output(SISO)linear output error models with multi-thresho...This paper addresses a modified auxiliary model stochastic gradient recursive parameter identification algorithm(M-AM-SGRPIA)for a class of single input single output(SISO)linear output error models with multi-threshold quantized observations.It proves the convergence of the designed algorithm.A pattern-moving-based system dynamics description method with hybrid metrics is proposed for a kind of practical single input multiple output(SIMO)or SISO nonlinear systems,and a SISO linear output error model with multi-threshold quantized observations is adopted to approximate the unknown system.The system input design is accomplished using the measurement technology of random repeatability test,and the probabilistic characteristic of the explicit metric value is employed to estimate the implicit metric value of the pattern class variable.A modified auxiliary model stochastic gradient recursive algorithm(M-AM-SGRA)is designed to identify the model parameters,and the contraction mapping principle proves its convergence.Two numerical examples are given to demonstrate the feasibility and effectiveness of the achieved identification algorithm.展开更多
In this paper,an adaptive backstepping control scheme is proposed for attitude tracking of non-rigid spacecraft in the presence of input quantization,inertial uncertainty and external disturbance.TThe control signal f...In this paper,an adaptive backstepping control scheme is proposed for attitude tracking of non-rigid spacecraft in the presence of input quantization,inertial uncertainty and external disturbance.TThe control signal for each actuator is quantized by sector-bounded quantizers,including the logarithmic quantizer and the hysteresis quantizer.By describing the impact of quantization in a new affine model and introducing a smooth function and a novel form of the control signal,the influence caused by input quantization and external disturbance is properly compensated for.Moreover,with the aid of the adaptive control technique,our approach can achieve attitude tracking without the explicit knowledge of inertial parameters.Unlike existing attitude control schemes for spacecraft,in this paper,the quantization parameters can be unknown,and the bounds of inertial parameters and disturbance are also not needed.In addition to proving the stability of the closed-loop system,the relationship between the control performance and design parameters is analyzed.Simulation results are presented to illustrate the effectiveness of the proposed scheme.展开更多
A modified harmony search algorithm with co-evolutional control parameters(DEHS), applied through differential evolution optimization, is proposed. In DEHS, two control parameters, i.e., harmony memory considering rat...A modified harmony search algorithm with co-evolutional control parameters(DEHS), applied through differential evolution optimization, is proposed. In DEHS, two control parameters, i.e., harmony memory considering rate and pitch adjusting rate, are encoded as a symbiotic individual of an original individual(i.e., harmony vector). Harmony search operators are applied to evolving the original population. DE is applied to co-evolving the symbiotic population based on feedback information from the original population. Thus, with the evolution of the original population in DEHS, the symbiotic population is dynamically and self-adaptively adjusted, and real-time optimum control parameters are obtained. The proposed DEHS algorithm has been applied to various benchmark functions and two typical dynamic optimization problems. The experimental results show that the performance of the proposed algorithm is better than that of other HS variants. Satisfactory results are obtained in the application.展开更多
The finite-time synchronization of fractional-order multi-weighted complex networks(FMCNs)with uncertain parameters and external disturbances is studied.Firstly,based on fractional calculus characteristics and Lyapuno...The finite-time synchronization of fractional-order multi-weighted complex networks(FMCNs)with uncertain parameters and external disturbances is studied.Firstly,based on fractional calculus characteristics and Lyapunov stability theory,quantized controllers are designed to guarantee that FMCNs can achieve synchronization in a limited time with and without coupling delay,respectively.Then,appropriate parameter update laws are obtained to identify the uncertain parameters in FMCNs.Finally,numerical simulation examples are given to validate the correctness of the theoretical results.展开更多
Differential evolution(DE) demonstrates good convergence performance,but it is difficult to choose trial vector generation strategies and associated control parameter values.An improved method,self-adapting scalable D...Differential evolution(DE) demonstrates good convergence performance,but it is difficult to choose trial vector generation strategies and associated control parameter values.An improved method,self-adapting scalable DE(SSDE) algorithm,is proposed.Trial vector generation strategies and crossover probability are respectively self-adapted by two operators in this algorithm.Meanwhile,to enhance the convergence rate,vectors selected randomly with the optimal fitness values are introduced to guide searching direction.Benchmark problems are used to verify this algorithm.Compared with other well-known DE algorithms,experiment results indicate that this algorithm is better than other DE algorithms in terms of convergence rate and quality of optimization.展开更多
基金This work was supported by the National Natural Science Foundation of China(No.60375001)the High School Doctoral Foundation of China(NO.20030532004).
文摘Control parameters of original differential evolution （DE） are kept fixed throughout the entire evolutionary process. However, it is not an easy task to properly set control parameters in DE for different optiinization problems. According to the relative position of two different individual vectors selected to generate a difference vector in the searching place, a self-adapting strategy for the scale factor F of the difference vector is proposed. In terms of the convergence status of the target vector in the current population, a self-adapting crossover probability constant CR strategy is proposed. Therefore, good target vectors have a lower CFI while worse target vectors have a large CFI. At the same time, the mutation operator is modified to improve the convergence speed. The performance of these proposed approaches are studied with the use of some benchmark problems and applied to the trajectory planning of a three-joint redundant manipulator. Finally, the experiment results show that the proposed approaches can greatly improve robustness and convergence speed.
文摘With the development of multimedia presentation technology,image acquisition technology and the Internet industry,long-distance communication methods have changed from the previous letter,the audio to the current audio/video.And the proportion of video in work,study and entertainment keeps increasing,high-definition video is getting more and more attention.Due to the limits of the network environment and storage capacity,the original video must be encoded to be efficiently transmitted and stored.High Efficient Video Coding(HEVC)requires a large amount of time to recursively traverse all possible quantization parameter values of the coding unit in the adaptive quantization process.The optimal quantization parameter is calculated by comparing the rate distortion cost.In this paper,we propose a fast decision method for HEVC quantization parameters selection based on convolutional neural network,which saves video’s encoding time.
基金Supported by China Automobile Test Cycle Development Project(CATC2015)
文摘A variable parameter self-adaptive control strategy based on driving condition identification is proposed to take full advantage of the fuel saving potential of the plug-in hybrid electric bus(PHEB).Firstly,the principal component analysis(PCA)and the fuzzy c-means clustering(FCM)algorithm is used to construct the comprehensive driving cycle,congestion driving cycle,urban driving cycle and suburban driving cycle of Chinese urban buses.Secondly,an improved particle swarm optimization(IPSO)algorithm is proposed,and is used to optimize the control parameters of PHEB under different driving cycles,respectively.Then,the variable parameter self-adaptive control strategy based on driving condition identification is given.Finally,for an actual running vehicle,the driving condition is identified by relevance vector machine(RVM),and the corresponding control parameters are selected to control the vehicle.The simulation results show that the fuel consumption of using the variable parameter self-adaptive control strategy is reduced by 4.2% compared with that of the fixed parameter control strategy,and the feasibility of the variable parameter self-adaptive control strategy is verified.
基金supported by the National Key R&D Program of China(No.2021YFB0301200)National Natural Science Foundation of China(No.62025208).
文摘Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning paradigm.While this approach allows models to specialize in specific tasks with reduced training costs,the substantial memory requirements during fine-tuning present a barrier to broader deployment.Parameter-Efficient Fine-Tuning(PEFT)techniques,such as Low-Rank Adaptation(LoRA),and parameter quantization methods have emerged as solutions to address these challenges by optimizing memory usage and computational efficiency.Among these,QLoRA,which combines PEFT and quantization,has demonstrated notable success in reducing memory footprints during fine-tuning,prompting the development of various QLoRA variants.Despite these advancements,the quantitative impact of key variables on the fine-tuning performance of quantized LLMs remains underexplored.This study presents a comprehensive analysis of these key variables,focusing on their influence across different layer types and depths within LLM architectures.Our investigation uncovers several critical findings:(1)Larger layers,such as MLP layers,can maintain performance despite reductions in adapter rank,while smaller layers,like self-attention layers,aremore sensitive to such changes;(2)The effectiveness of balancing factors depends more on specific values rather than layer type or depth;(3)In quantization-aware fine-tuning,larger layers can effectively utilize smaller adapters,whereas smaller layers struggle to do so.These insights suggest that layer type is a more significant determinant of fine-tuning success than layer depth when optimizing quantized LLMs.Moreover,for the same discount of trainable parameters,reducing the trainable parameters in a larger layer is more effective in preserving fine-tuning accuracy than in a smaller one.This study provides valuable guidance for more efficient fine-tuning strategies and opens avenues for further research into optimizing LLM fine-tuning in resource-constrained environments.
文摘The high-efficiency video coder(HEVC)is one of the most advanced techniques used in growing real-time multimedia applications today.However,they require large bandwidth for transmission through bandwidth,and bandwidth varies with different video sequences/formats.This paper proposes an adaptive information-based variable quantization matrix(AIVQM)developed for different video formats having variable energy levels.The quantization method is adapted based on video sequence using statistical analysis,improving bit budget,quality and complexity reduction.Further,to have precise control over bit rate and quality,a multi-constraint prune algorithm is proposed in the second stage of the AI-VQM technique for pre-calculating K numbers of paths.The same should be handy to selfadapt and choose one of the K-path automatically in dynamically changing bandwidth availability as per requirement after extensive testing of the proposed algorithm in the multi-constraint environment for multiple paths and evaluating the performance based on peak signal to noise ratio(PSNR),bit-budget and time complexity for different videos a noticeable improvement in rate-distortion(RD)performance is achieved.Using the proposed AIVQM technique,more feasible and efficient video sequences are achieved with less loss in PSNR than the variable quantization method(VQM)algorithm with approximately a rise of 10%–20%based on different video sequences/formats.
文摘Affine projection algorithm(APA)has been used to estimate the parameters of interior permanent magnet synchronous motor(IPMSM).However,there is not a strict guideline of choosing the stepsize of this algorithm to make sure that the results of parameter estimation are convergent.In order to solve such problem,self-adaptive stepsize affine projection algorithm for parameter estimation of IPMSM is proposed in this paper.Compared with traditional affine projection algorithm,this method can obtain the stepsize automatically based on the operation condition,which can ensure the convergence and celerity of the process of parameter estimation.Then,on the basis of self-adaptive stepsize affine projection algorithm,a novel parameter estimation method based on square-wave current injection is proposed.By this method,the error of estimated parameter caused by stator resistance,linkage magnetic flux and dead-time voltage can be reduced effectively.Finally,the proposed parameter estimation method is verified by experiments on a 2.2-kW IPMSM drive platform.
基金This work was supported by the National Natural Science Foundation of China(62076025).
文摘This paper addresses a modified auxiliary model stochastic gradient recursive parameter identification algorithm(M-AM-SGRPIA)for a class of single input single output(SISO)linear output error models with multi-threshold quantized observations.It proves the convergence of the designed algorithm.A pattern-moving-based system dynamics description method with hybrid metrics is proposed for a kind of practical single input multiple output(SIMO)or SISO nonlinear systems,and a SISO linear output error model with multi-threshold quantized observations is adopted to approximate the unknown system.The system input design is accomplished using the measurement technology of random repeatability test,and the probabilistic characteristic of the explicit metric value is employed to estimate the implicit metric value of the pattern class variable.A modified auxiliary model stochastic gradient recursive algorithm(M-AM-SGRA)is designed to identify the model parameters,and the contraction mapping principle proves its convergence.Two numerical examples are given to demonstrate the feasibility and effectiveness of the achieved identification algorithm.
文摘In this paper,an adaptive backstepping control scheme is proposed for attitude tracking of non-rigid spacecraft in the presence of input quantization,inertial uncertainty and external disturbance.TThe control signal for each actuator is quantized by sector-bounded quantizers,including the logarithmic quantizer and the hysteresis quantizer.By describing the impact of quantization in a new affine model and introducing a smooth function and a novel form of the control signal,the influence caused by input quantization and external disturbance is properly compensated for.Moreover,with the aid of the adaptive control technique,our approach can achieve attitude tracking without the explicit knowledge of inertial parameters.Unlike existing attitude control schemes for spacecraft,in this paper,the quantization parameters can be unknown,and the bounds of inertial parameters and disturbance are also not needed.In addition to proving the stability of the closed-loop system,the relationship between the control performance and design parameters is analyzed.Simulation results are presented to illustrate the effectiveness of the proposed scheme.
基金Project(2013CB733605)supported by the National Basic Research Program of ChinaProject(21176073)supported by the National Natural Science Foundation of China
文摘A modified harmony search algorithm with co-evolutional control parameters(DEHS), applied through differential evolution optimization, is proposed. In DEHS, two control parameters, i.e., harmony memory considering rate and pitch adjusting rate, are encoded as a symbiotic individual of an original individual(i.e., harmony vector). Harmony search operators are applied to evolving the original population. DE is applied to co-evolving the symbiotic population based on feedback information from the original population. Thus, with the evolution of the original population in DEHS, the symbiotic population is dynamically and self-adaptively adjusted, and real-time optimum control parameters are obtained. The proposed DEHS algorithm has been applied to various benchmark functions and two typical dynamic optimization problems. The experimental results show that the performance of the proposed algorithm is better than that of other HS variants. Satisfactory results are obtained in the application.
文摘The finite-time synchronization of fractional-order multi-weighted complex networks(FMCNs)with uncertain parameters and external disturbances is studied.Firstly,based on fractional calculus characteristics and Lyapunov stability theory,quantized controllers are designed to guarantee that FMCNs can achieve synchronization in a limited time with and without coupling delay,respectively.Then,appropriate parameter update laws are obtained to identify the uncertain parameters in FMCNs.Finally,numerical simulation examples are given to validate the correctness of the theoretical results.
基金National Natural Science Foundation of China (No. 70971020)
文摘Differential evolution(DE) demonstrates good convergence performance,but it is difficult to choose trial vector generation strategies and associated control parameter values.An improved method,self-adapting scalable DE(SSDE) algorithm,is proposed.Trial vector generation strategies and crossover probability are respectively self-adapted by two operators in this algorithm.Meanwhile,to enhance the convergence rate,vectors selected randomly with the optimal fitness values are introduced to guide searching direction.Benchmark problems are used to verify this algorithm.Compared with other well-known DE algorithms,experiment results indicate that this algorithm is better than other DE algorithms in terms of convergence rate and quality of optimization.
