In this paper, the method based on uniform design and neural network is proposed to model the complex system. In order to express the system characteristics all round, uniform design method is used to choose the model...In this paper, the method based on uniform design and neural network is proposed to model the complex system. In order to express the system characteristics all round, uniform design method is used to choose the modeling samples and obtain the overall information of the system;for the purpose of modeling the system or its characteristics, the artificial neural network is used to construct the model. Experiment indicates that this method can model the complex system effectively.展开更多
Newton＇s learning algorithm of NN is presented and realized. In theory, the convergence rate of learning algorithm of NN based on Newton＇s method must be faster than BP＇s and other learning algorithms, because the ...Newton＇s learning algorithm of NN is presented and realized. In theory, the convergence rate of learning algorithm of NN based on Newton＇s method must be faster than BP＇s and other learning algorithms, because the gradient method is linearly convergent while Newton＇s method has second order convergence rate. The fast computing algorithm of Hesse matrix of the cost function of NN is proposed and it is the theory basis of the improvement of Newton＇s learning algorithm. Simulation results show that the convergence rate of Newton＇s learning algorithm is high and apparently faster than the traditional BP method＇s, and the robustness of Newton＇s learning algorithm is also better than BP method＇ s.展开更多
General neural network inverse adaptive controller has two flaws: the first is the slow convergence speed; the second is the invalidation to the non-minimum phase system. These defects limit the scope in which the neu...General neural network inverse adaptive controller has two flaws: the first is the slow convergence speed; the second is the invalidation to the non-minimum phase system. These defects limit the scope in which the neural network inverse adaptive controller is used. We employ Davidon least squares in training the multi-layer feedforward neural network used in approximating the inverse model of plant to expedite the convergence, and then through constructing the pseudo-plant, a neural network inverse adaptive controller is put forward which is still effective to the nonlinear non-minimum phase system. The simulation results show the validity of this scheme.展开更多
The problem of passivity analysis is investigated for uncertain stochastic neural networks with discrete interval and distributed time-varying delays.The parameter uncertainties are assumed to be norm bounded and the ...The problem of passivity analysis is investigated for uncertain stochastic neural networks with discrete interval and distributed time-varying delays.The parameter uncertainties are assumed to be norm bounded and the delay is assumed to be time-varying and belongs to a given interval,which means that the lower and upper bounds of interval time-varying delays are available.By constructing proper Lyapunov-Krasovskii functional and employing a combination of the free-weighting matrix method and stochastic analysis technique,new delay-dependent passivity conditions are derived in terms of linear matrix inequalities（LMIs）.Finally,numerical examples are given to show the less conservatism of the proposed conditions.展开更多
This paper proposes an optimization algorithm based on a multi-loop control system with a neural network controller,in which the objective function that is used is the control plant of each sub-control system.To obtai...This paper proposes an optimization algorithm based on a multi-loop control system with a neural network controller,in which the objective function that is used is the control plant of each sub-control system.To obtain the global optimization solution from a control plant that has many local minimum points,a transformation function is presented.On the one hand,this approach changes a complex objective function into a simple function under the condition of an unchanged globally optimal solution,to find the global optimization solution more easily by using a multi-loop control system.On the other hand,a special neural network(in which the node function can be simply positioned locally)that is composed of multiple transformation functions is used as the controller,which reduces the possibility of falling into local minimum points.At the same time,a filled function is presented as a control law;it can jump out of a local minimum point and move to another local minimum point that has a smaller value of the objective function.Finally,18 simulation examples are provided to show the effectiveness of the proposed method.展开更多
目的针对模糊神经网络结构调整缺乏可解释性和网络参数优化精度不足等问题,提出一种基于神经元竞争机制和吸引子的三重竞争群优化算法的自组织模糊神经网络(selforganizing fuzzy neural network based on attractor triple competitive...目的针对模糊神经网络结构调整缺乏可解释性和网络参数优化精度不足等问题,提出一种基于神经元竞争机制和吸引子的三重竞争群优化算法的自组织模糊神经网络(selforganizing fuzzy neural network based on attractor triple competitive swarm optimization and neural competition,NCSOFNN-ATCSO)设计方法。方法首先,提出一种基于神经元竞争的网络调整机制,赋予结构调整以生物学意义;其次,设计神经元竞争力指标与Axin2基因表达水平,并采用单边Jacobi分解规则层神经元输出矩阵,以准确量化各神经元对于网络的重要性,提高神经元竞争的准确性;最后,为提高网络预测精度,采用基于动态吸引子的三重竞争群优化算法优化网络参数,引入三重竞争机制提升网络优化速度,并设计动态吸引子,以找到更优的参数向量。结果通过基准测试函数验证ATCSO算法性能,所提算法效率和精度更高;通过时间序列预测实验测试所提网络模型,相较于各对比模型,NCSOFNN-ATCSO精度更高且结构更精简。此外,将所提网络模型应用于出水氨氮的质量浓度预测,能够较为准确地预测出水氨氮质量浓度。结论与其他网络模型相比,所提NCSOFNN-ATCSO能够得到结构紧凑且预测精度较高的网络模型。展开更多
文摘In this paper, the method based on uniform design and neural network is proposed to model the complex system. In order to express the system characteristics all round, uniform design method is used to choose the modeling samples and obtain the overall information of the system;for the purpose of modeling the system or its characteristics, the artificial neural network is used to construct the model. Experiment indicates that this method can model the complex system effectively.
文摘Newton＇s learning algorithm of NN is presented and realized. In theory, the convergence rate of learning algorithm of NN based on Newton＇s method must be faster than BP＇s and other learning algorithms, because the gradient method is linearly convergent while Newton＇s method has second order convergence rate. The fast computing algorithm of Hesse matrix of the cost function of NN is proposed and it is the theory basis of the improvement of Newton＇s learning algorithm. Simulation results show that the convergence rate of Newton＇s learning algorithm is high and apparently faster than the traditional BP method＇s, and the robustness of Newton＇s learning algorithm is also better than BP method＇ s.
基金Tianjin Natural Science Foundation !983602011National 863/CIMS Research Foundation !863-511-945-010
文摘General neural network inverse adaptive controller has two flaws: the first is the slow convergence speed; the second is the invalidation to the non-minimum phase system. These defects limit the scope in which the neural network inverse adaptive controller is used. We employ Davidon least squares in training the multi-layer feedforward neural network used in approximating the inverse model of plant to expedite the convergence, and then through constructing the pseudo-plant, a neural network inverse adaptive controller is put forward which is still effective to the nonlinear non-minimum phase system. The simulation results show the validity of this scheme.
基金supported by Department of Science and Technology,New Delhi,India(SR/S4/MS:485/07)
文摘The problem of passivity analysis is investigated for uncertain stochastic neural networks with discrete interval and distributed time-varying delays.The parameter uncertainties are assumed to be norm bounded and the delay is assumed to be time-varying and belongs to a given interval,which means that the lower and upper bounds of interval time-varying delays are available.By constructing proper Lyapunov-Krasovskii functional and employing a combination of the free-weighting matrix method and stochastic analysis technique,new delay-dependent passivity conditions are derived in terms of linear matrix inequalities（LMIs）.Finally,numerical examples are given to show the less conservatism of the proposed conditions.
基金supported by the National Natural Science Foundation of China(61273190)
文摘This paper proposes an optimization algorithm based on a multi-loop control system with a neural network controller,in which the objective function that is used is the control plant of each sub-control system.To obtain the global optimization solution from a control plant that has many local minimum points,a transformation function is presented.On the one hand,this approach changes a complex objective function into a simple function under the condition of an unchanged globally optimal solution,to find the global optimization solution more easily by using a multi-loop control system.On the other hand,a special neural network(in which the node function can be simply positioned locally)that is composed of multiple transformation functions is used as the controller,which reduces the possibility of falling into local minimum points.At the same time,a filled function is presented as a control law;it can jump out of a local minimum point and move to another local minimum point that has a smaller value of the objective function.Finally,18 simulation examples are provided to show the effectiveness of the proposed method.
文摘目的针对模糊神经网络结构调整缺乏可解释性和网络参数优化精度不足等问题,提出一种基于神经元竞争机制和吸引子的三重竞争群优化算法的自组织模糊神经网络(selforganizing fuzzy neural network based on attractor triple competitive swarm optimization and neural competition,NCSOFNN-ATCSO)设计方法。方法首先,提出一种基于神经元竞争的网络调整机制,赋予结构调整以生物学意义;其次,设计神经元竞争力指标与Axin2基因表达水平,并采用单边Jacobi分解规则层神经元输出矩阵,以准确量化各神经元对于网络的重要性,提高神经元竞争的准确性;最后,为提高网络预测精度,采用基于动态吸引子的三重竞争群优化算法优化网络参数,引入三重竞争机制提升网络优化速度,并设计动态吸引子,以找到更优的参数向量。结果通过基准测试函数验证ATCSO算法性能,所提算法效率和精度更高;通过时间序列预测实验测试所提网络模型,相较于各对比模型,NCSOFNN-ATCSO精度更高且结构更精简。此外,将所提网络模型应用于出水氨氮的质量浓度预测,能够较为准确地预测出水氨氮质量浓度。结论与其他网络模型相比,所提NCSOFNN-ATCSO能够得到结构紧凑且预测精度较高的网络模型。
