神经信息系统(neuro information systems,Neuro IS)是认知神经科学理论、方法和工具在信息系统领域的应用,从全新的方法论视角研究和解决信息系统中的相关问题.神经信息系统的研究主要集中在系统设计与优化、信息服务与决策、社会网络...神经信息系统(neuro information systems,Neuro IS)是认知神经科学理论、方法和工具在信息系统领域的应用,从全新的方法论视角研究和解决信息系统中的相关问题.神经信息系统的研究主要集中在系统设计与优化、信息服务与决策、社会网络与互动这三大领域,主要的研究范式可分为情景实验的研究范式、心理学及决策科学经典任务应用的研究范式、多任务多方法结合三类.神经信息系统研究方法有效弥补了传统信息系统研究存在的不足,减少了应答偏误、实现了用户心理过程的准确测量并探索了用户决策的神经机制,发展和深化了信息系统的研究手段和理论基础.神经信息系统研究在补充和丰富现有理论的基础上,通过探索和发现传统信息系统领域中尚未解决以及存在争议的问题,揭示用户信息决策的机制,打开"黑箱",推动信息系统科学研究向"更客观,更深入"的方向发展.当前数字经济蓬勃发展,神经信息系统方向的发展为从事信息系统研究的学者提出了新的历史使命,创造了新的历史机遇.展开更多
To develop a better approach for spatial evaluation of drinking water quality, an intelligent evaluation method integrating a geographical information system(GIS) and an ant colony clustering algorithm(ACCA) was used....To develop a better approach for spatial evaluation of drinking water quality, an intelligent evaluation method integrating a geographical information system(GIS) and an ant colony clustering algorithm(ACCA) was used. Drinking water samples from 29 wells in Zhenping County, China, were collected and analyzed. 35 parameters on water quality were selected, such as chloride concentration, sulphate concentration, total hardness, nitrate concentration, fluoride concentration, turbidity, pH, chromium concentration, COD, bacterium amount, total coliforms and color. The best spatial interpolation methods for the 35 parameters were found and selected from all types of interpolation methods in GIS environment according to the minimum cross-validation errors. The ACCA was improved through three strategies, namely mixed distance function, average similitude degree and probability conversion functions. Then, the ACCA was carried out to obtain different water quality grades in the GIS environment. In the end, the result from the ACCA was compared with those from the competitive Hopfield neural network(CHNN) to validate the feasibility and effectiveness of the ACCA according to three evaluation indexes, which are stochastic sampling method, pixel amount and convergence speed. It is shown that the spatial water quality grades obtained from the ACCA were more effective, accurate and intelligent than those obtained from the CHNN.展开更多
文摘神经信息系统(neuro information systems,Neuro IS)是认知神经科学理论、方法和工具在信息系统领域的应用,从全新的方法论视角研究和解决信息系统中的相关问题.神经信息系统的研究主要集中在系统设计与优化、信息服务与决策、社会网络与互动这三大领域,主要的研究范式可分为情景实验的研究范式、心理学及决策科学经典任务应用的研究范式、多任务多方法结合三类.神经信息系统研究方法有效弥补了传统信息系统研究存在的不足,减少了应答偏误、实现了用户心理过程的准确测量并探索了用户决策的神经机制,发展和深化了信息系统的研究手段和理论基础.神经信息系统研究在补充和丰富现有理论的基础上,通过探索和发现传统信息系统领域中尚未解决以及存在争议的问题,揭示用户信息决策的机制,打开"黑箱",推动信息系统科学研究向"更客观,更深入"的方向发展.当前数字经济蓬勃发展,神经信息系统方向的发展为从事信息系统研究的学者提出了新的历史使命,创造了新的历史机遇.
基金Projects(41161020,41261026) supported by the National Natural Science Foundation of ChinaProject(BQD2012013) supported by the Research starting Funds for Imported Talents,Ningxia University,China+1 种基金Project(ZR1209) supported by the Natural Science Funds,Ningxia University,ChinaProject(NGY2013005) supported by the Key Science Project of Colleges and Universities in Ningxia,China
文摘To develop a better approach for spatial evaluation of drinking water quality, an intelligent evaluation method integrating a geographical information system(GIS) and an ant colony clustering algorithm(ACCA) was used. Drinking water samples from 29 wells in Zhenping County, China, were collected and analyzed. 35 parameters on water quality were selected, such as chloride concentration, sulphate concentration, total hardness, nitrate concentration, fluoride concentration, turbidity, pH, chromium concentration, COD, bacterium amount, total coliforms and color. The best spatial interpolation methods for the 35 parameters were found and selected from all types of interpolation methods in GIS environment according to the minimum cross-validation errors. The ACCA was improved through three strategies, namely mixed distance function, average similitude degree and probability conversion functions. Then, the ACCA was carried out to obtain different water quality grades in the GIS environment. In the end, the result from the ACCA was compared with those from the competitive Hopfield neural network(CHNN) to validate the feasibility and effectiveness of the ACCA according to three evaluation indexes, which are stochastic sampling method, pixel amount and convergence speed. It is shown that the spatial water quality grades obtained from the ACCA were more effective, accurate and intelligent than those obtained from the CHNN.
