介绍了采用结构化方法开发养老保险业务管理信息系统的过程。开发过程遵循了系统分析、系统设计、系统实施的开发步骤。采用Powerbuilder 8.0和SQL Server 2000分别作为前台开发工具和后台数据库管理系统。研制出的系统对养老保险业务...介绍了采用结构化方法开发养老保险业务管理信息系统的过程。开发过程遵循了系统分析、系统设计、系统实施的开发步骤。采用Powerbuilder 8.0和SQL Server 2000分别作为前台开发工具和后台数据库管理系统。研制出的系统对养老保险业务具有实用价值。展开更多
数据挖掘是一种从数据库中发现规则和知识的自动或半自动的方法。微软公司的数据挖掘解决方案基于OLE DB for Data Mining 规范,是在数据挖掘标准化方面的突破,它必将推动数据挖掘技术的快速发展和广泛应用。文中结合实例介绍了此规范...数据挖掘是一种从数据库中发现规则和知识的自动或半自动的方法。微软公司的数据挖掘解决方案基于OLE DB for Data Mining 规范,是在数据挖掘标准化方面的突破,它必将推动数据挖掘技术的快速发展和广泛应用。文中结合实例介绍了此规范及基于该规范的 数据挖掘应用开发架构。展开更多
In the goal optimization and control optimization process the problems with common artificial neural network algorithm are unsure convergence, insufficient post-training network precision, and slow training speed, in ...In the goal optimization and control optimization process the problems with common artificial neural network algorithm are unsure convergence, insufficient post-training network precision, and slow training speed, in which partial minimum value question tends to occur. This paper conducted an in-depth study on the causes of the limi-tations of the algorithm, presented a rapid artificial neural network algorithm, which is characterized by integrating multiple algorithms and by using their complementary advan-tages. The salient feature of the method is self-organization, which can effectively prevent the optimized results from tending to be partial minimum values. Overall optimization can be achieved with this method, goal function can be searched for in overall scope. With op-timization control of coal mine ventilator as a practical application, the paper proves that by integrating multiple artificial neural network algorithms, best control optimization and goal optimized can be achieved.展开更多
Function simulation,which is called virtual reality too,is popularly applied to solve uncertain problems.Good performance of hidden layers and perfect capability of function simulation make artificial neural networks ...Function simulation,which is called virtual reality too,is popularly applied to solve uncertain problems.Good performance of hidden layers and perfect capability of function simulation make artificial neural networks one of the best choices to simulate functions with form unknown.Inputs and outputs were used to train the structure of the ar- tificial neural network to make the outputs of network vary with the given inputs and keep consistent with the original data within tolerance.However,we couldn't get expected re- sults by using samples of a simple two-variable-model for the cause of dimensional differ- ence.The way of artificial neural networks to fit functions,which uses 'multi-dimensional surface' of high dimension to fit 'multi-dimensional line' of low dimension,was proved;the conclusion that good effects of fitting don't mean good function modeling when a dimen- sional difference exists was provided,and a suggestion of 'surface collecting' in practical engineering application was proposed when collecting useful data.展开更多
基金Supported by the Science Foundation of the Liaoning Province(2004C011)
文摘In the goal optimization and control optimization process the problems with common artificial neural network algorithm are unsure convergence, insufficient post-training network precision, and slow training speed, in which partial minimum value question tends to occur. This paper conducted an in-depth study on the causes of the limi-tations of the algorithm, presented a rapid artificial neural network algorithm, which is characterized by integrating multiple algorithms and by using their complementary advan-tages. The salient feature of the method is self-organization, which can effectively prevent the optimized results from tending to be partial minimum values. Overall optimization can be achieved with this method, goal function can be searched for in overall scope. With op-timization control of coal mine ventilator as a practical application, the paper proves that by integrating multiple artificial neural network algorithms, best control optimization and goal optimized can be achieved.
文摘Function simulation,which is called virtual reality too,is popularly applied to solve uncertain problems.Good performance of hidden layers and perfect capability of function simulation make artificial neural networks one of the best choices to simulate functions with form unknown.Inputs and outputs were used to train the structure of the ar- tificial neural network to make the outputs of network vary with the given inputs and keep consistent with the original data within tolerance.However,we couldn't get expected re- sults by using samples of a simple two-variable-model for the cause of dimensional differ- ence.The way of artificial neural networks to fit functions,which uses 'multi-dimensional surface' of high dimension to fit 'multi-dimensional line' of low dimension,was proved;the conclusion that good effects of fitting don't mean good function modeling when a dimen- sional difference exists was provided,and a suggestion of 'surface collecting' in practical engineering application was proposed when collecting useful data.
