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
首先采用风险价值(value at risk,VaR)方法根据历史数据计算出各月峰谷时段用电量的历史序列,并在一定的置信度下对谷段和峰段的用电量进行预测;然后采用区间数学方法构建了风险评估模型,以便对供电公司实行峰谷分时电价的风险进行衡量...首先采用风险价值(value at risk,VaR)方法根据历史数据计算出各月峰谷时段用电量的历史序列,并在一定的置信度下对谷段和峰段的用电量进行预测;然后采用区间数学方法构建了风险评估模型,以便对供电公司实行峰谷分时电价的风险进行衡量;最后采用该模型分析了某供电局的历史数据并衡量了其实施峰谷分时电价的风险,结果表明该模型不仅是有效的,而且还可以使需求侧管理更具可操作性。展开更多
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.
文摘首先采用风险价值(value at risk,VaR)方法根据历史数据计算出各月峰谷时段用电量的历史序列,并在一定的置信度下对谷段和峰段的用电量进行预测;然后采用区间数学方法构建了风险评估模型,以便对供电公司实行峰谷分时电价的风险进行衡量;最后采用该模型分析了某供电局的历史数据并衡量了其实施峰谷分时电价的风险,结果表明该模型不仅是有效的,而且还可以使需求侧管理更具可操作性。
