Aim To detect sensor failure in control system using a single sensor signal. Methods A neural predictor was designed based on a radial basis function network(RBFN), and the neural predictor learned the sensor sig...Aim To detect sensor failure in control system using a single sensor signal. Methods A neural predictor was designed based on a radial basis function network(RBFN), and the neural predictor learned the sensor signal on line with a hybrid algorithm composed of n means clustering and Kalman filter and then gave the estimation of the sensor signal at the next step. If the difference between the estimation and the actural values of the sensor signal exceeded a threshold, the sensor could be declared to have a failure. The choice of the failure detection threshold depends on the noise variance and the possible prediction error of neural predictor. Results and Conclusion\ The computer simulation results show the proposed method can detect sensor failure correctly for a gyro in an automotive engine.展开更多
Improving the efficiency of ship optimization is crucial for modem ship design. Compared with traditional methods, multidisciplinary design optimization （MDO） is a more promising approach. For this reason, Collabora...Improving the efficiency of ship optimization is crucial for modem ship design. Compared with traditional methods, multidisciplinary design optimization （MDO） is a more promising approach. For this reason, Collaborative Optimization （CO） is discussed and analyzed in this paper. As one of the most frequently applied MDO methods, CO promotes autonomy of disciplines while providing a coordinating mechanism guaranteeing progress toward an optimum and maintaining interdisciplinary compatibility. However, there are some difficulties in applying the conventional CO method, such as difficulties in choosing an initial point and tremendous computational requirements. For the purpose of overcoming these problems, optimal Latin hypercube design and Radial basis function network were applied to CO. Optimal Latin hypercube design is a modified Latin Hypercube design. Radial basis function network approximates the optimization model, and is updated during the optimization process to improve accuracy. It is shown by examples that the computing efficiency and robustness of this CO method are higher than with the conventional CO method.展开更多
The on-line estimation of the state of charge （SOC） of the batteries is important for the reliable running of the pure electric vehicle in practice. Because a nonlinear feature exists in the batteries and the radial...The on-line estimation of the state of charge （SOC） of the batteries is important for the reliable running of the pure electric vehicle in practice. Because a nonlinear feature exists in the batteries and the radial-basis-function neural network （RBF NN） has good characteristics to solve the nonlinear problem, a practical method for the SOC estimation of batteries based on the RBF NN with a small number of input variables and a simplified structure is proposed. Firstly, in this paper, the model of on-line SOC estimation with the RBF NN is set. Secondly, four important factors for estimating the SOC are confirmed based on the contribution analysis method, which simplifies the input variables of the RBF NN and enhttnces the real-time performance of estimation. FiItally, the pure electric buses with LiFePO4 Li-ion batteries running during the period of the 2010 Shanghai World Expo are considered as the experimental object. The performance of the SOC estimation is validated and evaluated by the battery data from the electric vehicle.展开更多
提出一种光强-波长模型和径向基函数网络(radial basis function network,RBFN)相融合的光谱共焦信号峰值提取算法,简称RBFN-I-λ。首先通过高斯拟合法拟合离散光谱响应信号的差分信号粗略得到初始峰值波长,然后基于泰勒近似法得到理想...提出一种光强-波长模型和径向基函数网络(radial basis function network,RBFN)相融合的光谱共焦信号峰值提取算法,简称RBFN-I-λ。首先通过高斯拟合法拟合离散光谱响应信号的差分信号粗略得到初始峰值波长,然后基于泰勒近似法得到理想峰值波长并计算初始峰值波长和理想峰值波长之间的波长差,最后利用RBFN-I-λ建立光谱共焦响应信号与波长描述误差之间的映射关系。实验结果表明,RBFN-I-λ算法的精度与传统抛物线法、质心法和高斯拟合法等方法相比,至少提升30%。展开更多
为应对当今供应链库存管理面临的牛鞭效应、两时间尺度特性和不确定性干扰等挑战,开发了一种基于径向基函数神经网络(radial basis function neural network,RBFNN)的两时间尺度供应链H_(∞)最优控制器。利用奇异摄动理论将原两时间尺...为应对当今供应链库存管理面临的牛鞭效应、两时间尺度特性和不确定性干扰等挑战,开发了一种基于径向基函数神经网络(radial basis function neural network,RBFNN)的两时间尺度供应链H_(∞)最优控制器。利用奇异摄动理论将原两时间尺度供应链模型分解为2个具有不同时间尺度的独立子系统;创新性地使用RBFNN在线近似补偿子系统的不确定项,进而采用H_(∞)控制来抑制RBFNN近似误差带来的不确定性。在理论层面上分析证明了所提方法的稳定性。通过一个电视机生产流程仿真案例,验证了所提方法相比2种其他两时间尺度问题解决方法,具有更高的跟踪控制精度和应用可行性。展开更多
为实现对船体分段焊接质量的有效管控,提出基于径向基函数神经网络(Radial Basis Function Neural Network,RBFNN)模型和异常检测的船体分段焊接质量溯源方法。从质量影响因素、不合格产品质量溯源方法和不合格产品质量溯源体系架构等...为实现对船体分段焊接质量的有效管控,提出基于径向基函数神经网络(Radial Basis Function Neural Network,RBFNN)模型和异常检测的船体分段焊接质量溯源方法。从质量影响因素、不合格产品质量溯源方法和不合格产品质量溯源体系架构等方面对船体分段焊接不合格产品质量溯源进行设计。从数据预处理、影响因素定位和影响因素排序等方面对船体分段焊接不合格产品质量溯源流程进行设置。经实例验证,所提出的方法可有效进行船体分段焊接质量溯源。展开更多
Characteristics of the Internet traffic data flow are studied based on the chaos theory. A phase space that is isometric with the network dynamic system is reconstructed by using the single variable time series of a n...Characteristics of the Internet traffic data flow are studied based on the chaos theory. A phase space that is isometric with the network dynamic system is reconstructed by using the single variable time series of a network flow. Some parameters, such as the correlative dimension and the Lyapunov exponent are calculated, and the chaos characteristic is proved to exist in Internet traffic data flows. A neural network model is construct- ed based on radial basis function （RBF） to forecast actual Internet traffic data flow. Simulation results show that, compared with other forecasts of the forward-feedback neural network, the forecast of the RBF neural network based on the chaos theory has faster learning capacity and higher forecasting accuracy.展开更多
A radial basis function network(RBF)has excellent generalization ability and approximation accuracy when its parameters are set appropriately.However,when relying only on traditional methods,it is difficult to obtain ...A radial basis function network(RBF)has excellent generalization ability and approximation accuracy when its parameters are set appropriately.However,when relying only on traditional methods,it is difficult to obtain optimal network parameters and construct a stable model as well.In view of this,a novel radial basis neural network(RBF-MLP)is proposed in this article.By connecting two networks to work cooperatively,the RBF’s parameters can be adjusted adaptively by the structure of the multi-layer perceptron(MLP)to realize the effect of the backpropagation updating error.Furthermore,a genetic algorithm is used to optimize the network’s hidden layer to confirm the optimal neurons(basis function)number automatically.In addition,a memristive circuit model is proposed to realize the neural network’s operation based on the characteristics of spin memristors.It is verified that the network can adaptively construct a network model with outstanding robustness and can stably achieve 98.33%accuracy in the processing of the Modified National Institute of Standards and Technology(MNIST)dataset classification task.The experimental results show that the method has considerable application value.展开更多
A description of the reliability evaluation of tactical network is given, which reflects not only the non-reliable factors of nodes and links but also the factors of network topological structure. On the basis of this...A description of the reliability evaluation of tactical network is given, which reflects not only the non-reliable factors of nodes and links but also the factors of network topological structure. On the basis of this description, a reliability prediction model and its algorithms are put forward based on the radial basis function neural network (RBFNN) for the tactical network. This model can carry out the non-linear mapping relationship between the network topological structure, the nodes reliabilities, the links reliabilities and the reliability of network. The results of simulation prove the effectiveness of this method in the reliability and the connectivity prediction for tactical network.展开更多
文摘Aim To detect sensor failure in control system using a single sensor signal. Methods A neural predictor was designed based on a radial basis function network(RBFN), and the neural predictor learned the sensor signal on line with a hybrid algorithm composed of n means clustering and Kalman filter and then gave the estimation of the sensor signal at the next step. If the difference between the estimation and the actural values of the sensor signal exceeded a threshold, the sensor could be declared to have a failure. The choice of the failure detection threshold depends on the noise variance and the possible prediction error of neural predictor. Results and Conclusion\ The computer simulation results show the proposed method can detect sensor failure correctly for a gyro in an automotive engine.
文摘Improving the efficiency of ship optimization is crucial for modem ship design. Compared with traditional methods, multidisciplinary design optimization （MDO） is a more promising approach. For this reason, Collaborative Optimization （CO） is discussed and analyzed in this paper. As one of the most frequently applied MDO methods, CO promotes autonomy of disciplines while providing a coordinating mechanism guaranteeing progress toward an optimum and maintaining interdisciplinary compatibility. However, there are some difficulties in applying the conventional CO method, such as difficulties in choosing an initial point and tremendous computational requirements. For the purpose of overcoming these problems, optimal Latin hypercube design and Radial basis function network were applied to CO. Optimal Latin hypercube design is a modified Latin Hypercube design. Radial basis function network approximates the optimization model, and is updated during the optimization process to improve accuracy. It is shown by examples that the computing efficiency and robustness of this CO method are higher than with the conventional CO method.
基金Project supported by the National High Technology Research and Development Program of China (Grant No. 2011AA110303)the Beijing Municipal Science & Technology Project,China (Grant No. Z111100064311001)
文摘The on-line estimation of the state of charge （SOC） of the batteries is important for the reliable running of the pure electric vehicle in practice. Because a nonlinear feature exists in the batteries and the radial-basis-function neural network （RBF NN） has good characteristics to solve the nonlinear problem, a practical method for the SOC estimation of batteries based on the RBF NN with a small number of input variables and a simplified structure is proposed. Firstly, in this paper, the model of on-line SOC estimation with the RBF NN is set. Secondly, four important factors for estimating the SOC are confirmed based on the contribution analysis method, which simplifies the input variables of the RBF NN and enhttnces the real-time performance of estimation. FiItally, the pure electric buses with LiFePO4 Li-ion batteries running during the period of the 2010 Shanghai World Expo are considered as the experimental object. The performance of the SOC estimation is validated and evaluated by the battery data from the electric vehicle.
文摘提出一种光强-波长模型和径向基函数网络(radial basis function network,RBFN)相融合的光谱共焦信号峰值提取算法,简称RBFN-I-λ。首先通过高斯拟合法拟合离散光谱响应信号的差分信号粗略得到初始峰值波长,然后基于泰勒近似法得到理想峰值波长并计算初始峰值波长和理想峰值波长之间的波长差,最后利用RBFN-I-λ建立光谱共焦响应信号与波长描述误差之间的映射关系。实验结果表明,RBFN-I-λ算法的精度与传统抛物线法、质心法和高斯拟合法等方法相比,至少提升30%。
文摘为应对当今供应链库存管理面临的牛鞭效应、两时间尺度特性和不确定性干扰等挑战,开发了一种基于径向基函数神经网络(radial basis function neural network,RBFNN)的两时间尺度供应链H_(∞)最优控制器。利用奇异摄动理论将原两时间尺度供应链模型分解为2个具有不同时间尺度的独立子系统;创新性地使用RBFNN在线近似补偿子系统的不确定项,进而采用H_(∞)控制来抑制RBFNN近似误差带来的不确定性。在理论层面上分析证明了所提方法的稳定性。通过一个电视机生产流程仿真案例,验证了所提方法相比2种其他两时间尺度问题解决方法,具有更高的跟踪控制精度和应用可行性。
文摘为实现对船体分段焊接质量的有效管控,提出基于径向基函数神经网络(Radial Basis Function Neural Network,RBFNN)模型和异常检测的船体分段焊接质量溯源方法。从质量影响因素、不合格产品质量溯源方法和不合格产品质量溯源体系架构等方面对船体分段焊接不合格产品质量溯源进行设计。从数据预处理、影响因素定位和影响因素排序等方面对船体分段焊接不合格产品质量溯源流程进行设置。经实例验证,所提出的方法可有效进行船体分段焊接质量溯源。
文摘Characteristics of the Internet traffic data flow are studied based on the chaos theory. A phase space that is isometric with the network dynamic system is reconstructed by using the single variable time series of a network flow. Some parameters, such as the correlative dimension and the Lyapunov exponent are calculated, and the chaos characteristic is proved to exist in Internet traffic data flows. A neural network model is construct- ed based on radial basis function （RBF） to forecast actual Internet traffic data flow. Simulation results show that, compared with other forecasts of the forward-feedback neural network, the forecast of the RBF neural network based on the chaos theory has faster learning capacity and higher forecasting accuracy.
文摘A radial basis function network(RBF)has excellent generalization ability and approximation accuracy when its parameters are set appropriately.However,when relying only on traditional methods,it is difficult to obtain optimal network parameters and construct a stable model as well.In view of this,a novel radial basis neural network(RBF-MLP)is proposed in this article.By connecting two networks to work cooperatively,the RBF’s parameters can be adjusted adaptively by the structure of the multi-layer perceptron(MLP)to realize the effect of the backpropagation updating error.Furthermore,a genetic algorithm is used to optimize the network’s hidden layer to confirm the optimal neurons(basis function)number automatically.In addition,a memristive circuit model is proposed to realize the neural network’s operation based on the characteristics of spin memristors.It is verified that the network can adaptively construct a network model with outstanding robustness and can stably achieve 98.33%accuracy in the processing of the Modified National Institute of Standards and Technology(MNIST)dataset classification task.The experimental results show that the method has considerable application value.
文摘A description of the reliability evaluation of tactical network is given, which reflects not only the non-reliable factors of nodes and links but also the factors of network topological structure. On the basis of this description, a reliability prediction model and its algorithms are put forward based on the radial basis function neural network (RBFNN) for the tactical network. This model can carry out the non-linear mapping relationship between the network topological structure, the nodes reliabilities, the links reliabilities and the reliability of network. The results of simulation prove the effectiveness of this method in the reliability and the connectivity prediction for tactical network.
