In this paper, a modeling algorithm developed by transferring the adaptive fuzzy inference neural network into an on-line real time algorithm, combining the algorithm with conventional system identification method and...In this paper, a modeling algorithm developed by transferring the adaptive fuzzy inference neural network into an on-line real time algorithm, combining the algorithm with conventional system identification method and applying them to separate identification of nonlinear multi-variable systems is introduced and discussed.展开更多
本文以黄河利津站和浙江省白溪水库的月径流水文序列为例,在自相关分析的基础上,建立自回归autoregression模型,并参照其结构建立了相应的resilient back propagation神经网络预报模型.比较结果显示:(1)resilient back propagation模型...本文以黄河利津站和浙江省白溪水库的月径流水文序列为例,在自相关分析的基础上,建立自回归autoregression模型,并参照其结构建立了相应的resilient back propagation神经网络预报模型.比较结果显示:(1)resilient back propagation模型的模拟预报结果与序列的自相关性有密切关系;(2)当序列有较好的自相关性时,可参照autoregression模型建立相应的resilient back propagation模型;(3)与传统autoregression模型相比,resilient back propagation模型能取得更高的预报精度;且随着预报步长增加,resilient back propagation模型的优势更加明显.展开更多
An improved pulse width modulation (PWM) neural network VLSI circuit for fault diagnosis is presented, which differs from the software-based fault diagnosis approach and exploits the merits of neural network VLSI circ...An improved pulse width modulation (PWM) neural network VLSI circuit for fault diagnosis is presented, which differs from the software-based fault diagnosis approach and exploits the merits of neural network VLSI circuit. A simple synapse multiplier is introduced, which has high precision, large linear range and less switching noise effects. A voltage-mode sigmoid circuit with adjustable gain is introduced for realization of different neuron activation functions. A voltage-pulse conversion circuit required for PWM is also introduced, which has high conversion precision and linearity. These 3 circuits are used to design a PWM VLSI neural network circuit to solve noise fault diagnosis for a main bearing. It can classify the fault samples directly. After signal processing, feature extraction and neural network computation for the analog noise signals including fault information,each output capacitor voltage value of VLSI circuit can be obtained, which represents Euclid distance between the corresponding fault signal template and the diagnosing signal, The real-time online recognition of noise fault signal can also be realized.展开更多
随着可再生能源在有源配电网中的渗透比例逐年增加,其带来的随机性、间歇性对已有调度策略产生了重大挑战。文章提出了一种基于多智能体深度强化学习的有源配电网经济调度策略,构建多区域能源自治框架,每个新能源自治区域对应一个智能体...随着可再生能源在有源配电网中的渗透比例逐年增加,其带来的随机性、间歇性对已有调度策略产生了重大挑战。文章提出了一种基于多智能体深度强化学习的有源配电网经济调度策略,构建多区域能源自治框架,每个新能源自治区域对应一个智能体,应用多智能体深度强化学习(multi-agent deep reinforcement learning,MADRL)算法解决各区域的协同经济调度问题,并对包含风机、储能设备的有源配电网进行区域建模,设定经济优化目标及运行约束条件,在多智能体深度确定性策略梯度(multi-agent deep deterministic policygradient,MADDPG)算法基础上,采用BiGRU(bidirectional gated recurrent unit)代替全连接层,进行新能源的出力预测,有效降低新能源波动性带来的影响,以改进的IEEE33测试系统进行算例分析,验证了所提策略的有效性和对比同类算法的优越性。展开更多
文摘In this paper, a modeling algorithm developed by transferring the adaptive fuzzy inference neural network into an on-line real time algorithm, combining the algorithm with conventional system identification method and applying them to separate identification of nonlinear multi-variable systems is introduced and discussed.
文摘本文以黄河利津站和浙江省白溪水库的月径流水文序列为例,在自相关分析的基础上,建立自回归autoregression模型,并参照其结构建立了相应的resilient back propagation神经网络预报模型.比较结果显示:(1)resilient back propagation模型的模拟预报结果与序列的自相关性有密切关系;(2)当序列有较好的自相关性时,可参照autoregression模型建立相应的resilient back propagation模型;(3)与传统autoregression模型相比,resilient back propagation模型能取得更高的预报精度;且随着预报步长增加,resilient back propagation模型的优势更加明显.
基金Supported by National Natural Science Foundation (60274015) the "863" Program of P, R. China (2002AA412420)
文摘An improved pulse width modulation (PWM) neural network VLSI circuit for fault diagnosis is presented, which differs from the software-based fault diagnosis approach and exploits the merits of neural network VLSI circuit. A simple synapse multiplier is introduced, which has high precision, large linear range and less switching noise effects. A voltage-mode sigmoid circuit with adjustable gain is introduced for realization of different neuron activation functions. A voltage-pulse conversion circuit required for PWM is also introduced, which has high conversion precision and linearity. These 3 circuits are used to design a PWM VLSI neural network circuit to solve noise fault diagnosis for a main bearing. It can classify the fault samples directly. After signal processing, feature extraction and neural network computation for the analog noise signals including fault information,each output capacitor voltage value of VLSI circuit can be obtained, which represents Euclid distance between the corresponding fault signal template and the diagnosing signal, The real-time online recognition of noise fault signal can also be realized.
文摘随着可再生能源在有源配电网中的渗透比例逐年增加,其带来的随机性、间歇性对已有调度策略产生了重大挑战。文章提出了一种基于多智能体深度强化学习的有源配电网经济调度策略,构建多区域能源自治框架,每个新能源自治区域对应一个智能体,应用多智能体深度强化学习(multi-agent deep reinforcement learning,MADRL)算法解决各区域的协同经济调度问题,并对包含风机、储能设备的有源配电网进行区域建模,设定经济优化目标及运行约束条件,在多智能体深度确定性策略梯度(multi-agent deep deterministic policygradient,MADDPG)算法基础上,采用BiGRU(bidirectional gated recurrent unit)代替全连接层,进行新能源的出力预测,有效降低新能源波动性带来的影响,以改进的IEEE33测试系统进行算例分析,验证了所提策略的有效性和对比同类算法的优越性。
