[Objective] The aim was to investigate the rhizome elongation growth of umbrella bamboo （Fargesia murielae） seedlings in China. [Method] The study was conducted in Liangfengya, Shennongjia National Nature Reserve. I...[Objective] The aim was to investigate the rhizome elongation growth of umbrella bamboo （Fargesia murielae） seedlings in China. [Method] The study was conducted in Liangfengya, Shennongjia National Nature Reserve. In the field investigation, six clumps of umbrella bamboo which grow independently were randomly selected and labeled. Rhizome elongation growth parameters, length and diameter of all the ages were measured. The age classes of bamboo rhizome were ascertained by age grade backtracking method. [Result] Field investigation suggested that at seedling phase, rhizome of umbrella bamboo prolonged very quickly under yearly time sequence, following an exponential curve. It indicated that although it was 15 years since the mother population died back, new generation had not been established its stable population yet. [Conclusion] Studying elongation regulation of umbrella bamboo may provide the theory understanding of life cycle of this long lived bamboo species.展开更多
The problem of path planning is studied for t he case for a mobile robot moving in a known environment. An aggressive algorith m using a description of the obstacles based on a neural network is proposed. Th e algorit...The problem of path planning is studied for t he case for a mobile robot moving in a known environment. An aggressive algorith m using a description of the obstacles based on a neural network is proposed. Th e algorithm allows to construct an optimal path which is piecewise linear with c hanging directions of the obstacles and the calculation speed for the proposed a lgorithm is comparatively fast. Simulation results and an application to a car_l ike robot 'Khepera' show the effectiveness of the proposed algorithm.展开更多
By combining properly the simulated annealing algorithm and the nonlinear programming neural network, a new hybrid method for comtrained global optimization is proposed in this paper. To maintain the applicability of ...By combining properly the simulated annealing algorithm and the nonlinear programming neural network, a new hybrid method for comtrained global optimization is proposed in this paper. To maintain the applicability of the simulated annealing algorithm used in the hybrid method as general as possible, the nonlinear programming neural network is employed at each iteration to find only a feasible solution to the original constrained problem rather than a local optimal solution. Such a feasible solution is obtained by solving an auxiliary optimization problem with a new objective function. The computational results for two numerical examples indicate that the proposed hybrid method for constrained global optimization is not only highly reliable but also much more effcient than the simulated annealing algorithm using the penalty function method to deal with the constraints.展开更多
Instead of existing methods,a recurrent neural network is conceived to deal with three stages of portfolio management.Mainly,a deterministic annealing neural network is proposed for the approach to portfolio problem,w...Instead of existing methods,a recurrent neural network is conceived to deal with three stages of portfolio management.Mainly,a deterministic annealing neural network is proposed for the approach to portfolio problem,which is a kind of quadratic programming.Finally,through a real example,we verify that the neural network model proposed in this paper is a good tool to solve the portfolio problem.展开更多
Reasons and realities such as being non-linear of dynamical equations,being lightweight and unstable nature of quadrotor,along with internal and external disturbances and parametric uncertainties,have caused that the ...Reasons and realities such as being non-linear of dynamical equations,being lightweight and unstable nature of quadrotor,along with internal and external disturbances and parametric uncertainties,have caused that the controller design for these quadrotors is considered the challenging issue of the day.In this work,an adaptive sliding mode controller based on neural network is proposed to control the altitude of a quadrotor.The error and error derivative of the altitude of a quadrotor are the inputs of neural network and altitude sliding surface variable is its output.Neural network estimates the sliding surface variable adaptively according to the conditions of quadrotor and sets the altitude of a quadrotor equal to the desired value.The proposed controller stability has been proven by Lyapunov theory and it is shown that all system states reach to sliding surface and are remaining in it.The superiority of the proposed control method has been proven by comparison and simulation results.展开更多
基金Supported by the National Natural Science Foundation of China (31070370)~~
文摘[Objective] The aim was to investigate the rhizome elongation growth of umbrella bamboo （Fargesia murielae） seedlings in China. [Method] The study was conducted in Liangfengya, Shennongjia National Nature Reserve. In the field investigation, six clumps of umbrella bamboo which grow independently were randomly selected and labeled. Rhizome elongation growth parameters, length and diameter of all the ages were measured. The age classes of bamboo rhizome were ascertained by age grade backtracking method. [Result] Field investigation suggested that at seedling phase, rhizome of umbrella bamboo prolonged very quickly under yearly time sequence, following an exponential curve. It indicated that although it was 15 years since the mother population died back, new generation had not been established its stable population yet. [Conclusion] Studying elongation regulation of umbrella bamboo may provide the theory understanding of life cycle of this long lived bamboo species.
文摘The problem of path planning is studied for t he case for a mobile robot moving in a known environment. An aggressive algorith m using a description of the obstacles based on a neural network is proposed. Th e algorithm allows to construct an optimal path which is piecewise linear with c hanging directions of the obstacles and the calculation speed for the proposed a lgorithm is comparatively fast. Simulation results and an application to a car_l ike robot 'Khepera' show the effectiveness of the proposed algorithm.
文摘By combining properly the simulated annealing algorithm and the nonlinear programming neural network, a new hybrid method for comtrained global optimization is proposed in this paper. To maintain the applicability of the simulated annealing algorithm used in the hybrid method as general as possible, the nonlinear programming neural network is employed at each iteration to find only a feasible solution to the original constrained problem rather than a local optimal solution. Such a feasible solution is obtained by solving an auxiliary optimization problem with a new objective function. The computational results for two numerical examples indicate that the proposed hybrid method for constrained global optimization is not only highly reliable but also much more effcient than the simulated annealing algorithm using the penalty function method to deal with the constraints.
基金Supported by the National Science Foundatin of China (No.79670 0 64)
文摘Instead of existing methods,a recurrent neural network is conceived to deal with three stages of portfolio management.Mainly,a deterministic annealing neural network is proposed for the approach to portfolio problem,which is a kind of quadratic programming.Finally,through a real example,we verify that the neural network model proposed in this paper is a good tool to solve the portfolio problem.
基金authorities of East Tehran Branch,Islamic Azad University,Tehran,Iran,for providing support and necessary facilities
文摘Reasons and realities such as being non-linear of dynamical equations,being lightweight and unstable nature of quadrotor,along with internal and external disturbances and parametric uncertainties,have caused that the controller design for these quadrotors is considered the challenging issue of the day.In this work,an adaptive sliding mode controller based on neural network is proposed to control the altitude of a quadrotor.The error and error derivative of the altitude of a quadrotor are the inputs of neural network and altitude sliding surface variable is its output.Neural network estimates the sliding surface variable adaptively according to the conditions of quadrotor and sets the altitude of a quadrotor equal to the desired value.The proposed controller stability has been proven by Lyapunov theory and it is shown that all system states reach to sliding surface and are remaining in it.The superiority of the proposed control method has been proven by comparison and simulation results.
