We propose a new approach for analyzing the global asymptotic stability of the extended discrete-time bidirectional associative memory （BAM） neural networks. By using the Euler rule, we discretize the continuous-tim...We propose a new approach for analyzing the global asymptotic stability of the extended discrete-time bidirectional associative memory （BAM） neural networks. By using the Euler rule, we discretize the continuous-time BAM neural networks as the extended discrete-time BAM neural networks with non-threshold activation functions. Here we present some conditions under which the neural networks have unique equilibrium points. To judge the global asymptotic stability of the equilibrium points, we introduce a new neural network model - standard neural network model （SNNM）. For the SNNMs, we derive the sufficient conditions for the global asymptotic stability of the equilibrium points, which are formulated as some linear matrix inequalities （LMIs）. We transform the discrete-time BAM into the SNNM and apply the general result about the SNNM to the determination of global asymptotic stability of the discrete-time BAM. The approach proposed extends the known stability results, has lower conservativeness, can be verified easily, and can also be applied to other forms of recurrent neural networks.展开更多
大量的危险与可操作性分析(hazard and operability analysis,HAZOP)报告以纸质文档形式保存,难于复用、共享,同时基于计算机软件的分析结果也只有对应的分析软件才能识别,同样存在难于复用、共享的问题。针对此问题,本文提出了基于知...大量的危险与可操作性分析(hazard and operability analysis,HAZOP)报告以纸质文档形式保存,难于复用、共享,同时基于计算机软件的分析结果也只有对应的分析软件才能识别,同样存在难于复用、共享的问题。针对此问题,本文提出了基于知识本体的HAZOP信息标准化框架。该框架以知识本体和HAZOP分析国际标准IEC 61882为基础,抽提归纳了HAZOP的标准化信息模型,给出了模型的整体结构、模型中各元素的定义与关系。并在此基础上,提出了HAZOP信息标准化方法,采用BiLSTM神经网络对每一条HAZOP分析的记录进行标注、训练与识别,实现了人工HAZOP分析结果的自动识别与标准化。以某油品合成装置为例,对HAZOP信息标准化方法进行了验证,结果表明基于知识本体的HAZOP信息标准化框架可以自动实现分析结果的标准化,便于分析知识的共享与复用。展开更多
为了提高渔业数据单位捕捞努力量渔获量(catch per unite of effort,CPUE)标准化数据的质量和模型连续稳定预测能力,该文采用人工神经网络(artificial neural network,ANN)、回归树(regression trees,RT)、随机森林(random forest,RF)...为了提高渔业数据单位捕捞努力量渔获量(catch per unite of effort,CPUE)标准化数据的质量和模型连续稳定预测能力,该文采用人工神经网络(artificial neural network,ANN)、回归树(regression trees,RT)、随机森林(random forest,RF)和支持向量机(support vector machine,SVM)等机器学习方法和传统的广义线性模型(generalized linear model,GLM)等方法,对2000-2013年大西洋大眼金枪鱼(Thunnus obesus)延绳钓CPUE数据进行标准化。采用平均绝对误差、平均均方误差、3种相关系数(Pearson’s,Kendall’s和Spearman’s)和标准化均方误差等评价指标对不同模型标准化结果进行对比,寻找较优的标准化方法。研究结果表明,在验证数据集SVM方法得到的3种相关系数(0.596,0473和0.632)和RF(0.623,0.456,0.621)相似,高于RT(0.516,0.432和0.586)、ANN(0.428,0.249和0.365)和GLM(0.199,0.106和0.159)。SVM预测的均方误差(11.25)、平均绝对误差(2.107)和标准化均方误差(0.652)略低于RF(11.655,2.377和0.661),明显低于RT(14.999,2.434和0.801)、ANN(16.692,2.883和0.823)和GLM(16.517,2.777和0.993)。各项指标揭示SVM方法要优于其他4种方法,RF次之,GLM计算结果在所有方法中最差,不适合渔业数据CPUE标准化。SVM和RF方法应该被优先考虑用于渔业数据CPUE标准化。研究结果为渔业资源管理和保护提供更好的支持。展开更多
基金This project was supported by the National Natural Science Foundation of China (60074008) .
文摘We propose a new approach for analyzing the global asymptotic stability of the extended discrete-time bidirectional associative memory （BAM） neural networks. By using the Euler rule, we discretize the continuous-time BAM neural networks as the extended discrete-time BAM neural networks with non-threshold activation functions. Here we present some conditions under which the neural networks have unique equilibrium points. To judge the global asymptotic stability of the equilibrium points, we introduce a new neural network model - standard neural network model （SNNM）. For the SNNMs, we derive the sufficient conditions for the global asymptotic stability of the equilibrium points, which are formulated as some linear matrix inequalities （LMIs）. We transform the discrete-time BAM into the SNNM and apply the general result about the SNNM to the determination of global asymptotic stability of the discrete-time BAM. The approach proposed extends the known stability results, has lower conservativeness, can be verified easily, and can also be applied to other forms of recurrent neural networks.
文摘大量的危险与可操作性分析(hazard and operability analysis,HAZOP)报告以纸质文档形式保存,难于复用、共享,同时基于计算机软件的分析结果也只有对应的分析软件才能识别,同样存在难于复用、共享的问题。针对此问题,本文提出了基于知识本体的HAZOP信息标准化框架。该框架以知识本体和HAZOP分析国际标准IEC 61882为基础,抽提归纳了HAZOP的标准化信息模型,给出了模型的整体结构、模型中各元素的定义与关系。并在此基础上,提出了HAZOP信息标准化方法,采用BiLSTM神经网络对每一条HAZOP分析的记录进行标注、训练与识别,实现了人工HAZOP分析结果的自动识别与标准化。以某油品合成装置为例,对HAZOP信息标准化方法进行了验证,结果表明基于知识本体的HAZOP信息标准化框架可以自动实现分析结果的标准化,便于分析知识的共享与复用。
文摘为了提高渔业数据单位捕捞努力量渔获量(catch per unite of effort,CPUE)标准化数据的质量和模型连续稳定预测能力,该文采用人工神经网络(artificial neural network,ANN)、回归树(regression trees,RT)、随机森林(random forest,RF)和支持向量机(support vector machine,SVM)等机器学习方法和传统的广义线性模型(generalized linear model,GLM)等方法,对2000-2013年大西洋大眼金枪鱼(Thunnus obesus)延绳钓CPUE数据进行标准化。采用平均绝对误差、平均均方误差、3种相关系数(Pearson’s,Kendall’s和Spearman’s)和标准化均方误差等评价指标对不同模型标准化结果进行对比,寻找较优的标准化方法。研究结果表明,在验证数据集SVM方法得到的3种相关系数(0.596,0473和0.632)和RF(0.623,0.456,0.621)相似,高于RT(0.516,0.432和0.586)、ANN(0.428,0.249和0.365)和GLM(0.199,0.106和0.159)。SVM预测的均方误差(11.25)、平均绝对误差(2.107)和标准化均方误差(0.652)略低于RF(11.655,2.377和0.661),明显低于RT(14.999,2.434和0.801)、ANN(16.692,2.883和0.823)和GLM(16.517,2.777和0.993)。各项指标揭示SVM方法要优于其他4种方法,RF次之,GLM计算结果在所有方法中最差,不适合渔业数据CPUE标准化。SVM和RF方法应该被优先考虑用于渔业数据CPUE标准化。研究结果为渔业资源管理和保护提供更好的支持。
