[1] GONG L, JIN C. Fuzzy comprehensive evaluation for carrying capacity of regional water resources[J]. Water Resources Management, 2009,23(12):2505-2513.
[2] SAKIZADEH M. Artificial intelligence for the prediction of water quality index in groundwater systems[J]. Modeling Earth Systems & Environment, 2016,2(1):8.
[3] RIZO-DECELIS L D, PARDO-IG〖KG-*3〗〖XCU.TIF,XQ〗〖KG-*3〗ZQUIZA E, ANDREO B. Spatial prediction of water quality variables along a main river channel in presence of pollution hotspots[J]. Science of the Total Environment, 2017,605-606:276-290.
[4] 袁从贵. 最小二乘支持向量回归及其在水质预测中的应用研究[D]. 广州:广东工业大学,2012.
[5] 〖KG-*2〗QIAO J F, HOU Y, HAN H G. Optimal control for wastewater treatment process based on an adaptive multi-objective differential evolution algorithm[J]. Neural Computing & Applications, 2017(1):1-14.
[6] HAN H G, ZHANG L, HOU Y, et al. Nonlinear model predictive control based on a self-organizing recurrent neural network[J]. IEEE Transactions on Neural Networks & Learning Systems, 2016,27(2):402.
[7] RINALDI S, SONCINISESSA R, ROMANO P. Parameter estimation of Streeter-Phelps models[J]. Journal of the Environmental Engineering Division, 1979,105(1):75-88.
[8] SINGH K P, BASANT A, MALIK A, et al. Artificial neural network modeling of the river water quality: A case study[J]. Ecological Modelling, 2009,220(6):888-895.
[9] HALASSI A, OUAZAR D, TAIK A. RBF methods for solving laterally averaged Saint Venant equations: Application to eutrophication prevention through aeration[J]. International Journal of Computational Fluid Dynamics, 2016,29(9-10):464-477.
[10]刘坤,刘贤赵,王巍,等. 模糊概率神经网络水质评价模型及其应用[J]. 数学的实践与认识, 2006,36(12):138-144.
[11]袁从贵,张新政,陈旭. 基于偏互信息与定尺度最小二乘支持向量机的咸潮预测模型[C]//第三十届中国控制会议. 2011:1482-1486.
[12]吴青,刘三阳,杜晶.回归型模糊最小二乘支持向量机[J]. 西安电子科技大学学报(自然科学版), 2007,34(5):773-778.
[13]LIU J L, LI J P, XU W X, et al. A weighted Lq adaptive least squares support vector machine classifiers: Robust and sparse approximation[J]. Expert Systems with Applications, 2011,38(3):2253-2259.
[14]温雯,郝志峰,邵壮丰. 迭代重加权最小二乘支持向量机快速算法研究[J]. 计算机科学, 2010,37(8):224-228.
[15]王定成,姜斌. 在线稀疏最小二乘支持向量机回归的研究[J]. 控制与决策, 2007,22(2):132-136.
[16]李洪超,王伟刚,董雪梅. 基于M-LS-SVR的变压器油中溶解气体浓度预测[J]. 电气技术, 2016(1):76-80.
[17]郭振凯,宋召青,毛剑琴. 一种改进的在线最小二乘支持向量机回归算法[J]. 控制与决策, 2009,24(1):145-149.
[18]阎辉,张学工,李衍达. 支持向量机与最小二乘关系研究[J]. 清华大学学报(自然科学版), 2001,41(9):77-80.
[19]KIM S E, SEO I W. Artificial neural network ensemble modeling with conjunctive data clustering for water quality prediction in rivers[J]. Journal of Hydro-environment Research, 2015,9(3):325-339.
[20]BARZEGAR R, ADAMOWSKI J, MOGHADDAM A A. Application of wavelet-artificial intelligence hybrid models for water quality prediction: A case study in Aji-Chay River, Iran[J]. Stochastic Environmental Research & Risk Assessment, 2016,30(7):1797-1819.
[21]CHATTERJEE S, SARKAR S, DEY N, et al. Application of cuckoo search in water quality prediction using artificial neural network[J]. International Journal of Computational Intelligence Studies, 2017,6(2/3):229-244.
[22]DBRUYNE M, SERNEELS S, VERDONCK T. Robustified least squares support vector classification[J]. Journal of Chemometrics, 2009,23(9):479-486.
[23]范玉刚,李平,宋执环. 动态加权最小二乘支持向量机[J]. 控制与决策, 2006,21(10):1129-1134. |