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1586 JOURNAL OF COMPUTERS, VOL. 8, NO. 6, JUNE 2013 option prices. The second point is to propose the model of maximizing the return under constrains of variance efficiency and shortfall preference structure in the robust counterpart, taking account of uncertain inputs. It extends the general portfolio model, putting forward some feasible suggestions to investors. ACKNOWLEDGMENT This research is supported by a Project Supported by Scientific Research Fund of Hunan Provincial Education Department (12C0749). REFERENCES [1] Alexander, S., T.F. Coleman and Yuying Li, Derivative Portfolio Hedging Based on CVaR, Journal of Banking and Finance, 34,pp.343-350, 2006. [2] Christos Papahristodoulou, Options strategies with linear programming, European Journal of Operational Research, 157, pp.246–256, 2004. [3] Mehment Horasanli.Hedging stragy for a portofolio of options and stocks with linear programming. Applied mathematics and computation, 199, pp.804-810,2008. [4] Pei-wang Gao. Options strategies with the risk adjustment. European Journal of Operational Research, 192, pp.975– 980, 2009. [5] Gerhard Scheuenstuhl, Rudi Zagst. Integrated portfolio management with options. European Journal of Operational Research, 185, pp.1477–1500, 2008. [6] Wets, R.J.B., Stochastic Programming. In: Nemhauser, G.L.,Rinnooy Kan, A.H.G., Todd, M.J., Handbooks in Operations Research and Management Science, Optimization, 1,pp.573–625, (Chapter VIII), 1991. [7] Steve Zymler, Robust portfolio optimization with derivative insurance guarantees. European Journal of Operational Research, 210, pp. 410–424, 2011. [8] A.L.Soyster, Convex programming with set-inclusive constraints and applications to inexact linear programming. Operations Research, 21, pp.1154-1157, 1973. [9] BenTal, A., Nemirovski, A., Robust optimization – methodology and alications. Math. Program., Ser. B, 92,pp.453–480,2002. [10] A,Nemilrovski A.Robust solutions of uncertain linear programs[J].Operations Research Letters, 25( 1), pp.1- 3,1999. [11] Steve Zymler. Robust Portfolio Optimization with Derivative Insurance Guarantees, 42, pp.1244-1265, 2010. [12] Ai-fan Ling, Cheng-xian Xu. Robust portfolio selection inving options under a ‘‘marginal + joint’’ ellipsoidal uncertainty set. Journal of Computational and Applied Mathematics, 236, pp. 3373–3393, 2012. [13] S. Zymler, B. Rustem.Robust Portfolio Optimization with Derivative Insurance Guarantees. www.comiswf.eu. 2009.4.11, pp.1-31. [14] Gerhard Scheuenstuhl, Rudi Zagst. Integrated portfolio management with options. European Journal of Operational Research,185, pp. 1477–1500,2008. [15] V.K. Chopra, W.T. Ziemba, The effect of errors in means, variances and covariances on optimal portfolio choice, Journal of Portfolio Management ,19(2),pp.6–11,1993. [16] L. El Ghaoui, M. Oks, and F. Outstry. Worst-case value-atrisk and robust portfolio optimization: A conic programming approach. Operations Research, 51(4), pp. 543-556, 2003. [17] M. S. Lobo, L. Vandenberghe, S. Boyd, and H. Lebret. Applications of second-order cone programming. Linear Algebra and its Applications, 284(1), pp.193-228, 1998. [18] Christine Gregory, Ken Darby-Dowman. Robust optimization and portfolio selection: The cost of robustness. European Journal of Operational Research 212, pp.417– 428, 2011. [19] Ben-Tal, A., Nemirovski, A., Robust convex optimization. Mathematics of Operations Research 23 (4), pp. 769–805, 1998. [20] S. Ceria and R. Stubbs. Incorporating estimation errors into portfolio selection: Robust portfolio construction. Journal of Asset Management, 7(2), pp. 109–127, 2006. [21] Anna G.Q., Alberto z. Robust optimization of conditional value at risk and portfolio selection. Journal of Banking & Finance, 32, pp. 2046–2056, 2008. [22] Wu, Jianwu,Functional verification methodology of complex electronics system based modeling and simulation. Journal of Computers, vol 5, no9, pp.1343-1347, 2010. [23] Mizouni,Simulation-based feature selection for software requirements baseline. Journal of Software, vol 7, no7, pp. 1440-1450, 2012. [24] Aijiu Chen. The Meso-level Numerical Experiment Research of the Mechanics Properties of Recycled Concrete. Journal of Software, vol 7.no 9, pp.1932-1940, 2012. Xing Yu was born in 1981. From 2000.9.1 to 2004,7.1, she studied at Department of mathematics and applied mathematics of Yangtze University, received Bachelor of Science Degree, From 2004.9.1 to 2007,1, she studied at Department of mathematics and applied mathematics of Huazhong University of science and technology, and earned a Master of Science Degree. From 2007,3, she is working at Hunan university of humanities Science and Technology, studying aim at financial mathematics, mathematical model. © 2013 ACADEMY PUBLISHER
JOURNAL OF COMPUTERS, VOL. 8, NO. 6, JUNE 2013 1587 A Novel Water Quality Assessment Method Based on Combination BP Neural Network Model and Fuzzy System Ming Xue Chang Chun Institute of Technology Chang Chun, China,130012 Email: xueming_net@sina.com Abstract—As the forefront of complex nonlinear science and artificial intelligence science, artificial neural network has began to be applied in the field of water quality control and planning step by step. According to the fuzzy feature of water quality information, this paper proposes a membership degree Back-Propagation network (MDBP) for water quality assessment with combining fuzzy mathematics and artificial neural network. The proposed MDBP model combines the merits of artificial neural network method and fuzzy evaluation method, which overcomes effectively the shortcoming of other assessment methods. With improving the accuracy and reliability of the assessment method, the method has a higher flexibility than other conventional approach and its programs have a better adaptability and more convenient application. The assessment method is closer to the reality with considering the continuity of the changes of water quality environment. Index Terms—Water Quality, Fuzzy Mathematics, Back- Propagation Neural Network, Assessment Method I. INTRODUCTION The water quality assessment is basic program to plan and manage water quality and important base of computing water environment capacity and controlling water pollutant, which shows the total information of water environment quality. In practice, there are many assessment methods used to water quality assessment. For example, the integrated index approach shows the uncertain characters of water quality changes, which holds the needs of water quality function classification. The practice shows that all of these used methods need to suppose subjective parameters and concrete assessment mode, so the assessment results always have obviously subjectivity and restrained applicability. In theory, the artificial neural network method with potentiality can solve the problem. As for the artificial neural theory, the function of learning and memorizing can provide the basic theory and methods for water quality assessment mode and classification problem. In the reference [1] the un-point pollutant sources drainage area is assessed by using the method of Bayesian concepts and combining artificial neutral network. In reference [2-4], the Back- Propagation network model with multi-input, multioutput and multi-layer is adopt to assess integrated water quality, and the qualitative description is used in water quality classification. But the shortcoming of this method is that the output mode must be obtained not by learning but artificially loading. Thus the assessment results can not be objective, direct and compact enough. In this paper, a new water quality assessment method is studied, which can be so much more effective and objective to overcome the shortcoming of the present artificial neural network method. A membership degree Back-Propagation network for water quality assessment with combining fuzzy mathematics and artificial neural network is proposed, which combines the merits of artificial neural network method and fuzzy evaluation method, and then the model overcomes effectively the shortcoming of other assessment methods. So the assessment method is closer to the reality with considering the continuity of the changes of water quality environment. The experiment and analysis show that the new water quality assessment method which combines BP neural network model and fuzzy system is effective. II. THE PRINCIPLE OF BACK PROPAGATION NEURAL NETWORK MODEL A. The Basic Structure of Back Propagation Network Model In 1985, Rumelhart and Meclelland proposed Back Propagation neural network model. Error Back Propagation usually called BP network in short, which is one of the most widely applied neural network model. [5]From the structure, BP network is typical multi-layer network which has not only input layer nodes and output layer nodes, but also one layer or multi-layer recessive nodes. In BP network, the consecutive layers are complete connected, but no connections in different nodes of same layer. [6] The structure of the BP neural network model with three layers is shown as fig.1. In the BP neural network model, the weigh coefficients between different layers can be adjusted automatically. Except for the input layer, the process units in other layers have nonlinear input/output connection. That is to say, the characteristic © 2013 ACADEMY PUBLISHER doi:10.4304/jcp.8.6.1587-1593
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(Contents Continued from Back Cover
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