In this paper,we study the optimal quadrature problem with Hermite-Birkhoff type,on the Sobolev class(R)defined on whole red axis,and we give an optimal algorithm and determite its optimal error.
This paper proposes a novel iterative algorithm for optimal design of non-frequency-selective Finite Impulse Response(FIR) digital filters based on the windowing method.Different from the traditional optimization conc...This paper proposes a novel iterative algorithm for optimal design of non-frequency-selective Finite Impulse Response(FIR) digital filters based on the windowing method.Different from the traditional optimization concept of adjusting the window or the filter order in the windowing design of an FIR digital filter,the key idea of the algorithm is minimizing the approximation error by succes-sively modifying the design result through an iterative procedure under the condition of a fixed window length.In the iterative procedure,the known deviation of the designed frequency response in each iteration from the ideal frequency response is used as a reference for the next iteration.Because the approximation error can be specified variably,the algorithm is applicable for the design of FIR digital filters with different technical requirements in the frequency domain.A design example is employed to illustrate the efficiency of the algorithm.展开更多
In this article, we consider the faster than Nyquist(FTN) technology in aspects of the application of the Viterbi algorithm(VA). Finite in time optimal FTN signals are used to provide a symbol rate higher than the &qu...In this article, we consider the faster than Nyquist(FTN) technology in aspects of the application of the Viterbi algorithm(VA). Finite in time optimal FTN signals are used to provide a symbol rate higher than the "Nyquist barrier" without any encoding. These signals are obtained as the solutions of the corresponding optimization problem. Optimal signals are characterized by intersymbol interference(ISI). This fact leads to significant bit error rate(BER) performance degradation for "classical" forms of signals. However, ISI can be controlled by the restriction of the optimization problem. So we can use optimal signals in conditions of increased duration and an increased symbol rate without significant energy losses. The additional symbol rate increase leads to the increase of the reception algorithm complexity. We consider the application of VA for optimal FTN signals reception. The application of VA for receiving optimal FTN signals with increased duration provides close to the potential performance of BER,while the symbol rate is twice above the Nyquist limit.展开更多
[Objective] The research aimed to simplify the traditional method and gain the method which could directly construct the comprehensive rainstorm intensity formula.[Method] The particle swarm optimization was used to o...[Objective] The research aimed to simplify the traditional method and gain the method which could directly construct the comprehensive rainstorm intensity formula.[Method] The particle swarm optimization was used to optimize the parameters of uniform comprehensive rainstorm intensity formula in every return period and directly construct the comprehensive rainstorm intensity formula.Moreover,took the comprehensive rainstorm intensity formula which was established by the hourly precipitation data in wuhu City as an example,the calculation result compared with the computed result of traditional method.[Result] The calculation result precision of particle swarm algorithm was higher than the traditional method,and the calculation process was simpler.[Conclusion] The particle swarm algorithm could directly construct the comprehensive rainstorm intensity formula.展开更多
The magic formula(MF)tire model is a semi-empirical tire model that can precisely simulate tire behavior.The heuristic optimization algorithm is typically used for parameter identification of the MF tire model.To avoi...The magic formula(MF)tire model is a semi-empirical tire model that can precisely simulate tire behavior.The heuristic optimization algorithm is typically used for parameter identification of the MF tire model.To avoid the defect of the traditional heuristic optimization algorithm that can easily fall into the local optimum,a parameter identification method based on the Fibonacci tree optimization(FTO)algorithm is proposed,which is used to identify the parameters of the MF tire model.The proposed method establishes the basic structure of the Fibonacci tree alternately through global and local searches and completes optimization accordingly.The global search rule in the original FTO was modified to improve its efficiency.The results of independent repeated experiments on two typical multimodal function optimizations and the parameter identification results showed that FTO was not sensitive to the initial values.In addition,it had a better global optimization performance than genetic algorithm(GA)and particle swarm optimization(PSO).The root mean square error values optimized with FTO were 5.09%,10.22%,and 3.98%less than the GA,and 6.04%,4.47%,and 16.42%less than the PSO in pure lateral and longitudinal forces,and pure aligning torque parameter identification.The parameter identification method based on FTO was found to be effective.展开更多
Taking the accelerometer installation errors into consideration, the attitude optimization algorithm of Gyro Free Inertial Meastement Unit （GFIMU） is studied in the high spinning condition in this paper. A ten-accel...Taking the accelerometer installation errors into consideration, the attitude optimization algorithm of Gyro Free Inertial Meastement Unit （GFIMU） is studied in the high spinning condition in this paper. A ten-accelerometer configuration is designed so as to establish a mathematical model to acquire the angular speeds in the case of installation errors. Precision of the algorithm is evaluated by using damping GaussNewton method. A large amotmt of sinmlation results show that ff the accelertlmter＇s angleinstallation errors main-tain small （〈5°）, the errors of attitude angles can be limited within ±1°. Hence, the algorithm has a great applicable value in engineering.展开更多
Extended Kalman Filter(EKF)algorithm is widely used in parameter estimation for nonlinear systems.The estimation precision is sensitively dependent on EKF’s initial state covariance matrix and state noise matrix.The ...Extended Kalman Filter(EKF)algorithm is widely used in parameter estimation for nonlinear systems.The estimation precision is sensitively dependent on EKF’s initial state covariance matrix and state noise matrix.The grid optimization method is always used to find proper initial matrix for off-line estimation.However,the grid method has the draw back being time consuming hence,coarse grid followed by a fine grid method is adopted.To further improve efficiency without the loss of estimation accuracy,we propose a genetic algorithm for the coarse grid optimization in this paper.It is recognized that the crossover rate and mutation rate are the main influencing factors for the performance of the genetic algorithm,so sensitivity experiments for these two factors are carried out and a set of genetic algorithm parameters with good adaptability were selected by testing with several gyros’experimental data.Experimental results show that the proposed algorithm has higher efficiency and better estimation accuracy than the traversing grid algorithm.展开更多
Quadrature formulas are considered for classes of smooth functions Wpr, Bpr,(?) with bounded mixed derivative or difference. For the classes of functions indicated above, the result that quadrature formulas constructe...Quadrature formulas are considered for classes of smooth functions Wpr, Bpr,(?) with bounded mixed derivative or difference. For the classes of functions indicated above, the result that quadrature formulas constructed with the help of number-theoretic methods are optimal (in the sense of order) is proved, and the optimal order of the error estimates is obtained.展开更多
Constrained nonlinear optimization problems are well known as very difficult problems. In this paper, we present a new algorithm for solving such problems. Our proposed algorithm combines the Branch-and-Bound algorith...Constrained nonlinear optimization problems are well known as very difficult problems. In this paper, we present a new algorithm for solving such problems. Our proposed algorithm combines the Branch-and-Bound algorithm and Lipschitz constant to limit the search area effectively;this is essential for solving constrained nonlinear optimization problems. We obtain a more appropriate Lipschitz constant by applying the formula manipulation system of each divided area. Therefore, we obtain a better approximate solution without using a lot of searching points. The efficiency of our proposed algorithm has been shown by the results of some numerical experiments.展开更多
The problem of optimal periodic pulse jamming design for a quadrature phase shift keying（QPSK）communication system is investigated.First a closed-form bit-error-rate（BER）of QPSK system under the jamming of pulse s...The problem of optimal periodic pulse jamming design for a quadrature phase shift keying（QPSK）communication system is investigated.First a closed-form bit-error-rate（BER）of QPSK system under the jamming of pulse signal is derived.Then the asymptotic performance of the derived BER is analyzed as the signal-to-noise ratio（SNR）grows to infinity.In order to maximize the BER of the QPSK system,the optimal parameters of periodic pulse jamming signal,including the duty cycle and signal-tojamming power ratio（SJR）,are found out.Numerical results are presented to verify our analytical results and the optimality of our design.展开更多
This paper presents an innovative and effective control strategy tailored for a deregulated,diversified energy system involving multiple interconnected area.Each area integrates a unique mix of power generation techno...This paper presents an innovative and effective control strategy tailored for a deregulated,diversified energy system involving multiple interconnected area.Each area integrates a unique mix of power generation technologies:Area 1 combines thermal,hydro,and distributed generation;Area 2 utilizes a blend of thermal units,distributed solar technologies(DST),and hydro power;andThird control area hosts geothermal power station alongside thermal power generation unit and hydropower units.The suggested control system employs a multi-layered approach,featuring a blended methodology utilizing the Tilted Integral Derivative controller(TID)and the Fractional-Order Integral method to enhance performance and stability.The parameters of this hybrid TID-FOI controller are finely tuned using an advanced optimization method known as the Walrus Optimization Algorithm(WaOA).Performance analysis reveals that the combined TID-FOI controller significantly outperforms the TID and PID controllers when comparing their dynamic response across various system configurations.The study also incorporates investigation of redox flow batteries within the broader scope of energy storage applications to assess their impact on system performance.In addition,the research explores the controller’s effectiveness under different power exchange scenarios in a deregulated market,accounting for restrictions on generation ramp rates and governor hysteresis effects in dynamic control.To ensure the reliability and resilience of the presented methodology,the system transitions and develops across a broad range of varying parameters and stochastic load fluctuation.To wrap up,the study offers a pioneering control approach-a hybrid TID-FOI controller optimized via the Walrus Optimization Algorithm(WaOA)-designed for enhanced stability and performance in a complex,three-region hybrid energy system functioning within a deregulated framework.展开更多
A grating eddy current displacement sensor(GECDS) can be used in a watertight electronic transducer to realize long range displacement or position measurement with high accuracy in difficult industry conditions.The pa...A grating eddy current displacement sensor(GECDS) can be used in a watertight electronic transducer to realize long range displacement or position measurement with high accuracy in difficult industry conditions.The parameters optimization of the sensor is essential for economic and efficient production.This paper proposes a method to combine an artificial neural network(ANN) and a genetic algorithm(GA) for the sensor parameters optimization.A neural network model is developed to map the complex relationship between design parameters and the nonlinearity error of the GECDS,and then a GA is used in the optimization process to determine the design parameter values,resulting in a desired minimal nonlinearity error of about 0.11%.The calculated nonlinearity error is 0.25%.These results show that the proposed method performs well for the parameters optimization of the GECDS.展开更多
The optimization of cognitive radio(CR)system using an enhanced firefly algorithm(EFA)is presented in this work.The Firefly algorithm(FA)is a nature-inspired algorithm based on the unique light-flashing behavior of fi...The optimization of cognitive radio(CR)system using an enhanced firefly algorithm(EFA)is presented in this work.The Firefly algorithm(FA)is a nature-inspired algorithm based on the unique light-flashing behavior of fireflies.It has already proved its competence in various optimization prob-lems,but it suffers from slow convergence issues.To improve the convergence performance of FA,a new variant named EFA is proposed.The effectiveness of EFA as a good optimizer is demonstrated by optimizing benchmark functions,and simulation results show its superior performance compared to biogeography-based optimization(BBO),bat algorithm,artificial bee colony,and FA.As an application of this algorithm to real-world problems,EFA is also applied to optimize the CR system.CR is a revolutionary technique that uses a dynamic spectrum allocation strategy to solve the spectrum scarcity problem.However,it requires optimization to meet specific performance objectives.The results obtained by EFA in CR system optimization are compared with results in the literature of BBO,simulated annealing,and genetic algorithm.Statistical results further prove that the proposed algorithm is highly efficient and provides superior results.展开更多
In the numerical prediction of weather or climate events,the uncertainty of the initial values and/or prediction models can bring the forecast result’s uncertainty.Due to the absence of true states,studies on this pr...In the numerical prediction of weather or climate events,the uncertainty of the initial values and/or prediction models can bring the forecast result’s uncertainty.Due to the absence of true states,studies on this problem mainly focus on the three subproblems of predictability,i.e.,the lower bound of the maximum predictable time,the upper bound of the prediction error,and the lower bound of the maximum allowable initial error.Aimed at the problem of the lower bound estimation of the maximum allowable initial error,this study first illustrates the shortcoming of the existing estimation,and then presents a new estimation based on the initial observation precision and proves it theoretically.Furthermore,the new lower bound estimations of both the two-dimensional ikeda model and lorenz96 model are obtained by using the cnop(conditional nonlinear optimal perturbation)method and a pso(particle swarm optimization)algorithm,and the estimated precisions are also analyzed.Besides,the estimations yielded by the existing and new formulas are compared;the results show that the estimations produced by the existing formula are often incorrect.展开更多
It is demonstrated that the recently introduced semantic intelligence spontaneously maintains bounded logical and quantal error on each and every semantic trajectory, unlike its algorithmic counterpart which is not ab...It is demonstrated that the recently introduced semantic intelligence spontaneously maintains bounded logical and quantal error on each and every semantic trajectory, unlike its algorithmic counterpart which is not able to. This result verifies the conclusion about the assignment of equal evolutionary value to the motion on the set of all the semantic trajectories sharing the same homeostatic pattern. The evolutionary value of permanent and spontaneous maintenance of boundedness of logical and quantal error on each and every semantic trajectory is to make available spontaneous maintenance of the notion of a kind intact in the long run.展开更多
Network planning is essential for the construction and the development of wireless networks. The network planning cannot be possible without an appropriate propagation model which in fact is its foundation. Initially ...Network planning is essential for the construction and the development of wireless networks. The network planning cannot be possible without an appropriate propagation model which in fact is its foundation. Initially used mainly for mobile radio networks, the optimization of propagation model is becoming essential for efficient deployment of the network in different types of environment, namely rural, suburban and urban especially with the emergence of concepts such as digital terrestrial television, smart cities, Internet of Things (IoT) with wide deployment for different use cases such as smart grid, smart metering of electricity, gas and water. In this paper we use an optimization algorithm that is inspired by the principles of magnetic field theory namely Magnetic Optimization Algorithm (MOA) to tune COST231-Hata propagation model. The dataset used is the result of drive tests carry out on field in the town of Limbe in Cameroon. We take into account the standard K-factor model and then use the MOA algorithm in order to set up a propagation model adapted to the physical environment of a town. The town of Limbe is used as an implementation case, but the proposed method can be used everywhere. The calculation of the root mean square error (RMSE) between the real data from the radio measurements and the prediction data obtained after the implementation of MOA allows the validation of the results. A comparative study between the value of the RMSE obtained by the new model and those obtained by the optimization using linear regression, by the standard COST231-Hata models, and the free space model is also done, this allows us to conclude that the new model obtained using MOA for the city of Limbe is better and more representative of this local environment than the standard COST231-Hata model. The new model obtained can be used for radio planning in the city of Limbé in Cameroon.展开更多
Ocean observations are inherently characterized by irregular temporal and spatial distributions,as well as heterogeneous spatial resolutions and error characteristics arising from the use of diverse observational plat...Ocean observations are inherently characterized by irregular temporal and spatial distributions,as well as heterogeneous spatial resolutions and error characteristics arising from the use of diverse observational platforms and techniques.To enable their application across a broad range of scientific and practical problems,it is essential to map these heterogeneous datasets into temporally and spatially consistent gridded products.Optimal Interpolation remains the most widely adopted algorithm for the mapping of oceanographic data.Two principal implementations of the optimal interpolation algorithm are commonly employed.The first,known as the basic optimal interpolation,is derived from the theory of optimal estimation and involves computationally intensive matrix operations,posing significant challenges when applied to high-dimensional problems.The second,referred to as the point-wise optimal interpolation,reduces computational complexity through point-wise estimation,thereby circumventing high-dimensional operations;however,this approach results in a substantially higher overall computational cost.In this study,a novel optimal interpolation algorithm is proposed that utilizes the Kronecker product to approximate the background error covariance matrix.This formulation enables the decomposition of high-dimensional matrix operations into smaller,computationally tractable sub-problems,thereby improving the scalability of optimal interpolation for large spatial domains with dense observational coverage.Building upon this framework,a multi-scale optimal interpolation method is further developed to enhance the integration of observational datasets with widely varying spatial resolutions,thereby improving the accuracy and applicability of the resulting gridded products.展开更多
Safety-critical control enables intelligent robots to have better secure operation and resistance to adverse environments, hence greatly enhancing their interaction and adaptability to the environment. In this paper, ...Safety-critical control enables intelligent robots to have better secure operation and resistance to adverse environments, hence greatly enhancing their interaction and adaptability to the environment. In this paper, we propose a safety-critical control scheme for robotic systems using an adaptive error elimination algorithm and optimization-based nonlinear optimal predictive control(NOPC) framework. The novelty of the proposed work lies in that an adaptive error elimination controller is designed to deal with the problem of stabilization of walking gait, which ensures that robot joint trajectory can compensate for the limitation of the template model. In order to be independent of system parameters and disturbances, a sliding mode controller is further designed under an uncertain environment. This approach takes into account simultaneously with foot position and orientation based on NOPC optimization. It tracks the modified trajectories constrained with the centroidal momentum dynamics. Finally, simulations is utilized to verify the efectiveness of the mentioned methods. The results indicate that the tracking efect of joint trajectory is better safety-critical nonlinear optimal predictive control with adaptive error elimination algorithm.展开更多
文摘In this paper,we study the optimal quadrature problem with Hermite-Birkhoff type,on the Sobolev class(R)defined on whole red axis,and we give an optimal algorithm and determite its optimal error.
基金the National Grand Fundamental Research 973 Program of China (No.2004CB318109)the National High-Technology Research and Development Plan of China (No.2006AA01Z452)
文摘This paper proposes a novel iterative algorithm for optimal design of non-frequency-selective Finite Impulse Response(FIR) digital filters based on the windowing method.Different from the traditional optimization concept of adjusting the window or the filter order in the windowing design of an FIR digital filter,the key idea of the algorithm is minimizing the approximation error by succes-sively modifying the design result through an iterative procedure under the condition of a fixed window length.In the iterative procedure,the known deviation of the designed frequency response in each iteration from the ideal frequency response is used as a reference for the next iteration.Because the approximation error can be specified variably,the algorithm is applicable for the design of FIR digital filters with different technical requirements in the frequency domain.A design example is employed to illustrate the efficiency of the algorithm.
基金supported by the Grant of the President of the Russian Federation for state support of young Russian scientists(agreementМК-1571.2019.8 No.075-15-2019-1155)。
文摘In this article, we consider the faster than Nyquist(FTN) technology in aspects of the application of the Viterbi algorithm(VA). Finite in time optimal FTN signals are used to provide a symbol rate higher than the "Nyquist barrier" without any encoding. These signals are obtained as the solutions of the corresponding optimization problem. Optimal signals are characterized by intersymbol interference(ISI). This fact leads to significant bit error rate(BER) performance degradation for "classical" forms of signals. However, ISI can be controlled by the restriction of the optimization problem. So we can use optimal signals in conditions of increased duration and an increased symbol rate without significant energy losses. The additional symbol rate increase leads to the increase of the reception algorithm complexity. We consider the application of VA for optimal FTN signals reception. The application of VA for receiving optimal FTN signals with increased duration provides close to the potential performance of BER,while the symbol rate is twice above the Nyquist limit.
基金Supported by The College Management Science Research Project of Chengdu University of Information Technology (CRF200804)The Project of Sichuan Education Department (07ZB014)
文摘[Objective] The research aimed to simplify the traditional method and gain the method which could directly construct the comprehensive rainstorm intensity formula.[Method] The particle swarm optimization was used to optimize the parameters of uniform comprehensive rainstorm intensity formula in every return period and directly construct the comprehensive rainstorm intensity formula.Moreover,took the comprehensive rainstorm intensity formula which was established by the hourly precipitation data in wuhu City as an example,the calculation result compared with the computed result of traditional method.[Result] The calculation result precision of particle swarm algorithm was higher than the traditional method,and the calculation process was simpler.[Conclusion] The particle swarm algorithm could directly construct the comprehensive rainstorm intensity formula.
基金the National Natural Science Foundation of China(No.11672127)the Army Research and Technology Project(No.AQA19001)the Fundamental Research Funds for the Central Universities(No.NP2020407)。
文摘The magic formula(MF)tire model is a semi-empirical tire model that can precisely simulate tire behavior.The heuristic optimization algorithm is typically used for parameter identification of the MF tire model.To avoid the defect of the traditional heuristic optimization algorithm that can easily fall into the local optimum,a parameter identification method based on the Fibonacci tree optimization(FTO)algorithm is proposed,which is used to identify the parameters of the MF tire model.The proposed method establishes the basic structure of the Fibonacci tree alternately through global and local searches and completes optimization accordingly.The global search rule in the original FTO was modified to improve its efficiency.The results of independent repeated experiments on two typical multimodal function optimizations and the parameter identification results showed that FTO was not sensitive to the initial values.In addition,it had a better global optimization performance than genetic algorithm(GA)and particle swarm optimization(PSO).The root mean square error values optimized with FTO were 5.09%,10.22%,and 3.98%less than the GA,and 6.04%,4.47%,and 16.42%less than the PSO in pure lateral and longitudinal forces,and pure aligning torque parameter identification.The parameter identification method based on FTO was found to be effective.
基金supported by National Key Laboratory for Electronic Measurement and Technology(No.9140C120401080C12)
文摘Taking the accelerometer installation errors into consideration, the attitude optimization algorithm of Gyro Free Inertial Meastement Unit （GFIMU） is studied in the high spinning condition in this paper. A ten-accelerometer configuration is designed so as to establish a mathematical model to acquire the angular speeds in the case of installation errors. Precision of the algorithm is evaluated by using damping GaussNewton method. A large amotmt of sinmlation results show that ff the accelertlmter＇s angleinstallation errors main-tain small （〈5°）, the errors of attitude angles can be limited within ±1°. Hence, the algorithm has a great applicable value in engineering.
文摘Extended Kalman Filter(EKF)algorithm is widely used in parameter estimation for nonlinear systems.The estimation precision is sensitively dependent on EKF’s initial state covariance matrix and state noise matrix.The grid optimization method is always used to find proper initial matrix for off-line estimation.However,the grid method has the draw back being time consuming hence,coarse grid followed by a fine grid method is adopted.To further improve efficiency without the loss of estimation accuracy,we propose a genetic algorithm for the coarse grid optimization in this paper.It is recognized that the crossover rate and mutation rate are the main influencing factors for the performance of the genetic algorithm,so sensitivity experiments for these two factors are carried out and a set of genetic algorithm parameters with good adaptability were selected by testing with several gyros’experimental data.Experimental results show that the proposed algorithm has higher efficiency and better estimation accuracy than the traversing grid algorithm.
基金Project supported by the National Natural Science Foundation of China and the Doctoral Program Foundation of the State Education Commission of China.
文摘Quadrature formulas are considered for classes of smooth functions Wpr, Bpr,(?) with bounded mixed derivative or difference. For the classes of functions indicated above, the result that quadrature formulas constructed with the help of number-theoretic methods are optimal (in the sense of order) is proved, and the optimal order of the error estimates is obtained.
文摘Constrained nonlinear optimization problems are well known as very difficult problems. In this paper, we present a new algorithm for solving such problems. Our proposed algorithm combines the Branch-and-Bound algorithm and Lipschitz constant to limit the search area effectively;this is essential for solving constrained nonlinear optimization problems. We obtain a more appropriate Lipschitz constant by applying the formula manipulation system of each divided area. Therefore, we obtain a better approximate solution without using a lot of searching points. The efficiency of our proposed algorithm has been shown by the results of some numerical experiments.
基金Supported by the National Natural Science Foundation of China(61271258)
文摘The problem of optimal periodic pulse jamming design for a quadrature phase shift keying（QPSK）communication system is investigated.First a closed-form bit-error-rate（BER）of QPSK system under the jamming of pulse signal is derived.Then the asymptotic performance of the derived BER is analyzed as the signal-to-noise ratio（SNR）grows to infinity.In order to maximize the BER of the QPSK system,the optimal parameters of periodic pulse jamming signal,including the duty cycle and signal-tojamming power ratio（SJR）,are found out.Numerical results are presented to verify our analytical results and the optimality of our design.
文摘This paper presents an innovative and effective control strategy tailored for a deregulated,diversified energy system involving multiple interconnected area.Each area integrates a unique mix of power generation technologies:Area 1 combines thermal,hydro,and distributed generation;Area 2 utilizes a blend of thermal units,distributed solar technologies(DST),and hydro power;andThird control area hosts geothermal power station alongside thermal power generation unit and hydropower units.The suggested control system employs a multi-layered approach,featuring a blended methodology utilizing the Tilted Integral Derivative controller(TID)and the Fractional-Order Integral method to enhance performance and stability.The parameters of this hybrid TID-FOI controller are finely tuned using an advanced optimization method known as the Walrus Optimization Algorithm(WaOA).Performance analysis reveals that the combined TID-FOI controller significantly outperforms the TID and PID controllers when comparing their dynamic response across various system configurations.The study also incorporates investigation of redox flow batteries within the broader scope of energy storage applications to assess their impact on system performance.In addition,the research explores the controller’s effectiveness under different power exchange scenarios in a deregulated market,accounting for restrictions on generation ramp rates and governor hysteresis effects in dynamic control.To ensure the reliability and resilience of the presented methodology,the system transitions and develops across a broad range of varying parameters and stochastic load fluctuation.To wrap up,the study offers a pioneering control approach-a hybrid TID-FOI controller optimized via the Walrus Optimization Algorithm(WaOA)-designed for enhanced stability and performance in a complex,three-region hybrid energy system functioning within a deregulated framework.
文摘A grating eddy current displacement sensor(GECDS) can be used in a watertight electronic transducer to realize long range displacement or position measurement with high accuracy in difficult industry conditions.The parameters optimization of the sensor is essential for economic and efficient production.This paper proposes a method to combine an artificial neural network(ANN) and a genetic algorithm(GA) for the sensor parameters optimization.A neural network model is developed to map the complex relationship between design parameters and the nonlinearity error of the GECDS,and then a GA is used in the optimization process to determine the design parameter values,resulting in a desired minimal nonlinearity error of about 0.11%.The calculated nonlinearity error is 0.25%.These results show that the proposed method performs well for the parameters optimization of the GECDS.
基金funded by King Saud University,Riyadh,Saudi Arabia.Researchers Supporting Proiect Number(RSP2023R167)King Saud University,Riyadh,Saudi Arabia.
文摘The optimization of cognitive radio(CR)system using an enhanced firefly algorithm(EFA)is presented in this work.The Firefly algorithm(FA)is a nature-inspired algorithm based on the unique light-flashing behavior of fireflies.It has already proved its competence in various optimization prob-lems,but it suffers from slow convergence issues.To improve the convergence performance of FA,a new variant named EFA is proposed.The effectiveness of EFA as a good optimizer is demonstrated by optimizing benchmark functions,and simulation results show its superior performance compared to biogeography-based optimization(BBO),bat algorithm,artificial bee colony,and FA.As an application of this algorithm to real-world problems,EFA is also applied to optimize the CR system.CR is a revolutionary technique that uses a dynamic spectrum allocation strategy to solve the spectrum scarcity problem.However,it requires optimization to meet specific performance objectives.The results obtained by EFA in CR system optimization are compared with results in the literature of BBO,simulated annealing,and genetic algorithm.Statistical results further prove that the proposed algorithm is highly efficient and provides superior results.
基金supported by the National Natural Science Foundation of China(Grant No.41331174)
文摘In the numerical prediction of weather or climate events,the uncertainty of the initial values and/or prediction models can bring the forecast result’s uncertainty.Due to the absence of true states,studies on this problem mainly focus on the three subproblems of predictability,i.e.,the lower bound of the maximum predictable time,the upper bound of the prediction error,and the lower bound of the maximum allowable initial error.Aimed at the problem of the lower bound estimation of the maximum allowable initial error,this study first illustrates the shortcoming of the existing estimation,and then presents a new estimation based on the initial observation precision and proves it theoretically.Furthermore,the new lower bound estimations of both the two-dimensional ikeda model and lorenz96 model are obtained by using the cnop(conditional nonlinear optimal perturbation)method and a pso(particle swarm optimization)algorithm,and the estimated precisions are also analyzed.Besides,the estimations yielded by the existing and new formulas are compared;the results show that the estimations produced by the existing formula are often incorrect.
文摘It is demonstrated that the recently introduced semantic intelligence spontaneously maintains bounded logical and quantal error on each and every semantic trajectory, unlike its algorithmic counterpart which is not able to. This result verifies the conclusion about the assignment of equal evolutionary value to the motion on the set of all the semantic trajectories sharing the same homeostatic pattern. The evolutionary value of permanent and spontaneous maintenance of boundedness of logical and quantal error on each and every semantic trajectory is to make available spontaneous maintenance of the notion of a kind intact in the long run.
文摘Network planning is essential for the construction and the development of wireless networks. The network planning cannot be possible without an appropriate propagation model which in fact is its foundation. Initially used mainly for mobile radio networks, the optimization of propagation model is becoming essential for efficient deployment of the network in different types of environment, namely rural, suburban and urban especially with the emergence of concepts such as digital terrestrial television, smart cities, Internet of Things (IoT) with wide deployment for different use cases such as smart grid, smart metering of electricity, gas and water. In this paper we use an optimization algorithm that is inspired by the principles of magnetic field theory namely Magnetic Optimization Algorithm (MOA) to tune COST231-Hata propagation model. The dataset used is the result of drive tests carry out on field in the town of Limbe in Cameroon. We take into account the standard K-factor model and then use the MOA algorithm in order to set up a propagation model adapted to the physical environment of a town. The town of Limbe is used as an implementation case, but the proposed method can be used everywhere. The calculation of the root mean square error (RMSE) between the real data from the radio measurements and the prediction data obtained after the implementation of MOA allows the validation of the results. A comparative study between the value of the RMSE obtained by the new model and those obtained by the optimization using linear regression, by the standard COST231-Hata models, and the free space model is also done, this allows us to conclude that the new model obtained using MOA for the city of Limbe is better and more representative of this local environment than the standard COST231-Hata model. The new model obtained can be used for radio planning in the city of Limbé in Cameroon.
基金The National Key Research and Development Program of China under contract No.2022YFF0801404.
文摘Ocean observations are inherently characterized by irregular temporal and spatial distributions,as well as heterogeneous spatial resolutions and error characteristics arising from the use of diverse observational platforms and techniques.To enable their application across a broad range of scientific and practical problems,it is essential to map these heterogeneous datasets into temporally and spatially consistent gridded products.Optimal Interpolation remains the most widely adopted algorithm for the mapping of oceanographic data.Two principal implementations of the optimal interpolation algorithm are commonly employed.The first,known as the basic optimal interpolation,is derived from the theory of optimal estimation and involves computationally intensive matrix operations,posing significant challenges when applied to high-dimensional problems.The second,referred to as the point-wise optimal interpolation,reduces computational complexity through point-wise estimation,thereby circumventing high-dimensional operations;however,this approach results in a substantially higher overall computational cost.In this study,a novel optimal interpolation algorithm is proposed that utilizes the Kronecker product to approximate the background error covariance matrix.This formulation enables the decomposition of high-dimensional matrix operations into smaller,computationally tractable sub-problems,thereby improving the scalability of optimal interpolation for large spatial domains with dense observational coverage.Building upon this framework,a multi-scale optimal interpolation method is further developed to enhance the integration of observational datasets with widely varying spatial resolutions,thereby improving the accuracy and applicability of the resulting gridded products.
基金supported by the National Natural Science Foundation of China (No. 61573260,No. 62073245,No. U1713211)
文摘Safety-critical control enables intelligent robots to have better secure operation and resistance to adverse environments, hence greatly enhancing their interaction and adaptability to the environment. In this paper, we propose a safety-critical control scheme for robotic systems using an adaptive error elimination algorithm and optimization-based nonlinear optimal predictive control(NOPC) framework. The novelty of the proposed work lies in that an adaptive error elimination controller is designed to deal with the problem of stabilization of walking gait, which ensures that robot joint trajectory can compensate for the limitation of the template model. In order to be independent of system parameters and disturbances, a sliding mode controller is further designed under an uncertain environment. This approach takes into account simultaneously with foot position and orientation based on NOPC optimization. It tracks the modified trajectories constrained with the centroidal momentum dynamics. Finally, simulations is utilized to verify the efectiveness of the mentioned methods. The results indicate that the tracking efect of joint trajectory is better safety-critical nonlinear optimal predictive control with adaptive error elimination algorithm.
