The present study involves estimation of open channel flow parameters having different bed materials invoking data of Gradual Varied Flow (GVF). Use of GVF data facilitates estimation of flow parameters. The necessary...The present study involves estimation of open channel flow parameters having different bed materials invoking data of Gradual Varied Flow (GVF). Use of GVF data facilitates estimation of flow parameters. The necessary data base was generated by conducting laboratory. In the present study, the efficacy of the Genetic Algorithm (GA) optimization technique is assessed in estimation of open channel flow parameters from the collected experimental data. Computer codes are developed to obtain optimal flow parameters Optimization Technique. Applicability, adequacy and robustness of the developed code are tested using sets of theoretical data generated by experimental work. A simulation model was developed to compute GVF depths at preselected discrete sections for given downstream head and discharge rate. This model is linked to an optimizer to estimate optimal value of decision variables. The proposed model is employed to a set of laboratory data for three bed materials. Application of proposed model reveals that optimal value of fitting parameter ranges from 1.42 to 1.48 as the material gets finer and optimal decision variable ranges from 0.015 to 0.024. The optimal estimates of Manning’s n of three different bed conditions of experimental channel appear to be higher than the corresponding reported/Strickler’s estimates.展开更多
A novel algorithm based on Radon-Ambiguity Transform (RAT) and Adaptive Signal Decomposition (ASD) is presented for the detection and parameter estimation of multicompo-nent Linear Frequency Modulated (LFM) signals. T...A novel algorithm based on Radon-Ambiguity Transform (RAT) and Adaptive Signal Decomposition (ASD) is presented for the detection and parameter estimation of multicompo-nent Linear Frequency Modulated (LFM) signals. The key problem lies in the chirplet estimation. Genetic algorithm is employed to search for the optimization parameter of chirplet. High estimation accuracy can be obtained even at low Signal-to-Noisc Ratio(SNR). Finally simulation results are provided to demonstrate the performance of the proposed algorithm.展开更多
The hybrid genetic algorithm is utilized to facilitate model parameter estimation.The tri-dimensional compression tests of soil are performed to supply experimental data for identifying nonlinear constitutive model of...The hybrid genetic algorithm is utilized to facilitate model parameter estimation.The tri-dimensional compression tests of soil are performed to supply experimental data for identifying nonlinear constitutive model of soil.In order to save computing time during parameter inversion,a new procedure to compute the calculated strains is presented by multi-linear simplification approach instead of finite element method(FEM).The real-coded hybrid genetic algorithm is developed by combining normal genetic algorithm with gradient-based optimization algorithm.The numerical and experimental results for conditioned soil are compared.The forecast strains based on identified nonlinear constitutive model of soil agree well with observed ones.The effectiveness and accuracy of proposed parameter estimation approach are validated.展开更多
In this work we introduce a modified version of the simple genetic algorithm (MGA) and will show the results of its application to two GMA power law models (a general theoretical branched pathway system and a mathemat...In this work we introduce a modified version of the simple genetic algorithm (MGA) and will show the results of its application to two GMA power law models (a general theoretical branched pathway system and a mathematical model of the amplification and responsiveness of the JAK2/STAT5 pathway representing an actual, experimentally studied system). The two case studies serve to illustrate the utility and potentialities of the MGA method for concerning parameter estimation in complex models of biological significance. The analysis of the results obtained from the application of the MGA algorithm allows an evaluation of the potentialities and shortcomings of the proposed algorithm when compared with other parameter estimation algorithm such as the simple genetic algorithm (SGA) and the simulated annealing (SA). MGA shows better performance in both studied cases than SGA and SA, either in the presence or absence of noise. It is suggested that these advantages are due to the fact that the objective function definition in the MGA could include the experimental error as a weight factor, thus minimizing the distance between the data and the predicted value. Actually, MGA is slightly slower that the SGA and the SA, but this limitation is compensated by its greater efficiency in finding objective values closer to the global optimum. Finally, MGA can lead to an early local optimum, but this shortcoming may be prevented by providing a great population diversity through the insertion of different selection processes.展开更多
A ship, as an object of course control, is characterized by a nonlinear function describing the static maneuvering characteristics. The backstepping method is one of the methods that can be used during the designing p...A ship, as an object of course control, is characterized by a nonlinear function describing the static maneuvering characteristics. The backstepping method is one of the methods that can be used during the designing process of a nonlinear course controller for ships. The method has been used for the purpose of designing two configurations of nonlinear controllers, which were then used to control the ship course. One of the configurations took dynamic characteristic of a steering gear into account during the designing stage. The parameters of the obtained nonlinear control structures have been tuned to optimise the operation of the control system. The optimisation process has been performed by means of genetic algorithms. The quality of operation of the designed control algorithms has been checked in simulation tests performed on the mathematical model of a tanker. The results of simulation experiments have been compared with the performance of the system containing a conventional proportional-derivative （PD） controller.展开更多
This paper proposes a Genetic Programming-Based Modeling (GPM) algorithm on chaotic time series. GP is used here to search for appropriate model structures in function space, and the Particle Swarm Optimization (PSO) ...This paper proposes a Genetic Programming-Based Modeling (GPM) algorithm on chaotic time series. GP is used here to search for appropriate model structures in function space, and the Particle Swarm Optimization (PSO) algorithm is used for Nonlinear Parameter Estimation (NPE) of dynamic model structures. In addition, GPM integrates the results of Nonlinear Time Series Analysis (NTSA) to adjust the parameters and takes them as the criteria of established models. Experiments showed the effectiveness of such improvements on chaotic time series modeling.展开更多
The initial alignment error equation of an INS (Inertial Navigation System) with large initial azimuth error has been derived and nonlinear characteristics are included. When azimuth error is fairly small, the nonline...The initial alignment error equation of an INS (Inertial Navigation System) with large initial azimuth error has been derived and nonlinear characteristics are included. When azimuth error is fairly small, the nonlinear equation can be reduced to a linear one. Extended Kalman filter, iterated filter and second order filter formulas are derived for the nonlinear state equation with linear measurement equation. Simulations results show that the accuracy of azimuth error estimation using extended Kalman filter is better than that of using standard Kalman filter while the iterated filter and second order filter can give even better estimation accuracy.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
Parameter setting for evolutionary algorithms is still an important issue in evolutionary computation. There are two main approaches to parameter setting： parameter tuning and parameter control. In this paper, we int...Parameter setting for evolutionary algorithms is still an important issue in evolutionary computation. There are two main approaches to parameter setting： parameter tuning and parameter control. In this paper, we introduce self-adaptive parameter control of a genetic algorithm based on Bayesian network learning and simulation. The nodes of this Bayesian network are genetic algorithm parameters to be controlled. Its structure captures probabilistie conditional （in）dependence relationships between the parameters. They are learned from the best individuals, i.e., the best configurations of the genetic algorithm. Individuals are evaluated by running the genetic algorithm for the respective parameter configuration. Since all these runs are time-consuming tasks, each genetic algorithm uses a small-sized population and is stopped before convergence. In this way promising individuals should not be lost. Experiments with an optimal search problem for simultaneous row and column orderings yield the same optima as state-of-the-art methods but with a sharp reduction in computational time. Moreover, our approach can cope with as yet unsolved high-dimensional problems.展开更多
The elastic parameters of soft tissues are important for medical diagnosis and virtual surgery simulation. In this study, we propose a novel nonlinear parameter estimation method for soft tissues. Firstly, an in-house...The elastic parameters of soft tissues are important for medical diagnosis and virtual surgery simulation. In this study, we propose a novel nonlinear parameter estimation method for soft tissues. Firstly, an in-house data acquisition platform was used to obtain external forces and their corresponding deformation values, To provide highly precise data for estimating nonlinear param- eters, the measured forces were corrected using the constructed weighted combination forecasting model based on a support vector machine （WCFM_SVM）. Secondly, a tetrahedral finite element parameter estimation model was established to describe the physical characteristics of soft tissues, using the substitution parameters of Young＇s modulus and Poisson＇s ratio to avoid solving compli- cated nonlinear problems. To improve the robustness of our model and avoid poor local minima, the initial parameters solved by a linear finite element model were introduced into the parameter estimation model. Finally, a self-adapting Levenberg-Marquardt （LM） algorithm was presented, which is capable of adaptively adjusting iterative parameters to solve the established parameter estimation model. The maximum absolute error of our WCFM SVM model was less than 0.03 Newton, resulting in more accurate forces in comparison with other correction models tested. The maximum absolute error between the calculated and measured nodal displacements was less than 1.5 mm, demonstrating that our nonlinear parameters are precise.展开更多
文摘The present study involves estimation of open channel flow parameters having different bed materials invoking data of Gradual Varied Flow (GVF). Use of GVF data facilitates estimation of flow parameters. The necessary data base was generated by conducting laboratory. In the present study, the efficacy of the Genetic Algorithm (GA) optimization technique is assessed in estimation of open channel flow parameters from the collected experimental data. Computer codes are developed to obtain optimal flow parameters Optimization Technique. Applicability, adequacy and robustness of the developed code are tested using sets of theoretical data generated by experimental work. A simulation model was developed to compute GVF depths at preselected discrete sections for given downstream head and discharge rate. This model is linked to an optimizer to estimate optimal value of decision variables. The proposed model is employed to a set of laboratory data for three bed materials. Application of proposed model reveals that optimal value of fitting parameter ranges from 1.42 to 1.48 as the material gets finer and optimal decision variable ranges from 0.015 to 0.024. The optimal estimates of Manning’s n of three different bed conditions of experimental channel appear to be higher than the corresponding reported/Strickler’s estimates.
文摘A novel algorithm based on Radon-Ambiguity Transform (RAT) and Adaptive Signal Decomposition (ASD) is presented for the detection and parameter estimation of multicompo-nent Linear Frequency Modulated (LFM) signals. The key problem lies in the chirplet estimation. Genetic algorithm is employed to search for the optimization parameter of chirplet. High estimation accuracy can be obtained even at low Signal-to-Noisc Ratio(SNR). Finally simulation results are provided to demonstrate the performance of the proposed algorithm.
基金Project(2007CB714006) supported by the National Basic Research Program of China Project(90815023) supported by the National Natural Science Foundation of China
文摘The hybrid genetic algorithm is utilized to facilitate model parameter estimation.The tri-dimensional compression tests of soil are performed to supply experimental data for identifying nonlinear constitutive model of soil.In order to save computing time during parameter inversion,a new procedure to compute the calculated strains is presented by multi-linear simplification approach instead of finite element method(FEM).The real-coded hybrid genetic algorithm is developed by combining normal genetic algorithm with gradient-based optimization algorithm.The numerical and experimental results for conditioned soil are compared.The forecast strains based on identified nonlinear constitutive model of soil agree well with observed ones.The effectiveness and accuracy of proposed parameter estimation approach are validated.
文摘In this work we introduce a modified version of the simple genetic algorithm (MGA) and will show the results of its application to two GMA power law models (a general theoretical branched pathway system and a mathematical model of the amplification and responsiveness of the JAK2/STAT5 pathway representing an actual, experimentally studied system). The two case studies serve to illustrate the utility and potentialities of the MGA method for concerning parameter estimation in complex models of biological significance. The analysis of the results obtained from the application of the MGA algorithm allows an evaluation of the potentialities and shortcomings of the proposed algorithm when compared with other parameter estimation algorithm such as the simple genetic algorithm (SGA) and the simulated annealing (SA). MGA shows better performance in both studied cases than SGA and SA, either in the presence or absence of noise. It is suggested that these advantages are due to the fact that the objective function definition in the MGA could include the experimental error as a weight factor, thus minimizing the distance between the data and the predicted value. Actually, MGA is slightly slower that the SGA and the SA, but this limitation is compensated by its greater efficiency in finding objective values closer to the global optimum. Finally, MGA can lead to an early local optimum, but this shortcoming may be prevented by providing a great population diversity through the insertion of different selection processes.
基金supported by Polish Ministry of Science and Higher Education (No. N514 015 32/1712)
文摘A ship, as an object of course control, is characterized by a nonlinear function describing the static maneuvering characteristics. The backstepping method is one of the methods that can be used during the designing process of a nonlinear course controller for ships. The method has been used for the purpose of designing two configurations of nonlinear controllers, which were then used to control the ship course. One of the configurations took dynamic characteristic of a steering gear into account during the designing stage. The parameters of the obtained nonlinear control structures have been tuned to optimise the operation of the control system. The optimisation process has been performed by means of genetic algorithms. The quality of operation of the designed control algorithms has been checked in simulation tests performed on the mathematical model of a tanker. The results of simulation experiments have been compared with the performance of the system containing a conventional proportional-derivative （PD） controller.
基金Project (Nos. 60174009 and 70071017) supported by the NationalNatural Science Foundation of China
文摘This paper proposes a Genetic Programming-Based Modeling (GPM) algorithm on chaotic time series. GP is used here to search for appropriate model structures in function space, and the Particle Swarm Optimization (PSO) algorithm is used for Nonlinear Parameter Estimation (NPE) of dynamic model structures. In addition, GPM integrates the results of Nonlinear Time Series Analysis (NTSA) to adjust the parameters and takes them as the criteria of established models. Experiments showed the effectiveness of such improvements on chaotic time series modeling.
文摘The initial alignment error equation of an INS (Inertial Navigation System) with large initial azimuth error has been derived and nonlinear characteristics are included. When azimuth error is fairly small, the nonlinear equation can be reduced to a linear one. Extended Kalman filter, iterated filter and second order filter formulas are derived for the nonlinear state equation with linear measurement equation. Simulations results show that the accuracy of azimuth error estimation using extended Kalman filter is better than that of using standard Kalman filter while the iterated filter and second order filter can give even better estimation accuracy.
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partially supported by the Spanish Ministry of Economy and Competitiveness under Grant No.TIN2010-20900-C04-04 and Cajal Blue Brain
文摘Parameter setting for evolutionary algorithms is still an important issue in evolutionary computation. There are two main approaches to parameter setting： parameter tuning and parameter control. In this paper, we introduce self-adaptive parameter control of a genetic algorithm based on Bayesian network learning and simulation. The nodes of this Bayesian network are genetic algorithm parameters to be controlled. Its structure captures probabilistie conditional （in）dependence relationships between the parameters. They are learned from the best individuals, i.e., the best configurations of the genetic algorithm. Individuals are evaluated by running the genetic algorithm for the respective parameter configuration. Since all these runs are time-consuming tasks, each genetic algorithm uses a small-sized population and is stopped before convergence. In this way promising individuals should not be lost. Experiments with an optimal search problem for simultaneous row and column orderings yield the same optima as state-of-the-art methods but with a sharp reduction in computational time. Moreover, our approach can cope with as yet unsolved high-dimensional problems.
基金supported by the National Natural Science Foundation of China (Grant No.61373107)Wuhan Science and Technology Program, China (Grant No.2016010101010022)
文摘The elastic parameters of soft tissues are important for medical diagnosis and virtual surgery simulation. In this study, we propose a novel nonlinear parameter estimation method for soft tissues. Firstly, an in-house data acquisition platform was used to obtain external forces and their corresponding deformation values, To provide highly precise data for estimating nonlinear param- eters, the measured forces were corrected using the constructed weighted combination forecasting model based on a support vector machine （WCFM_SVM）. Secondly, a tetrahedral finite element parameter estimation model was established to describe the physical characteristics of soft tissues, using the substitution parameters of Young＇s modulus and Poisson＇s ratio to avoid solving compli- cated nonlinear problems. To improve the robustness of our model and avoid poor local minima, the initial parameters solved by a linear finite element model were introduced into the parameter estimation model. Finally, a self-adapting Levenberg-Marquardt （LM） algorithm was presented, which is capable of adaptively adjusting iterative parameters to solve the established parameter estimation model. The maximum absolute error of our WCFM SVM model was less than 0.03 Newton, resulting in more accurate forces in comparison with other correction models tested. The maximum absolute error between the calculated and measured nodal displacements was less than 1.5 mm, demonstrating that our nonlinear parameters are precise.
