Download Advances in Multiple Objective and Goal Programming: by Rafael Caballero, Francisco Ruiz, Ralph E. Steuer (auth.), PDF

By Rafael Caballero, Francisco Ruiz, Ralph E. Steuer (auth.), Prof. Rafael Caballero, Prof. Francisco Ruiz, Prof. Ralph Steuer (eds.)

In the box of a number of standards selection making, this quantity covers the newest advances in a number of aim and aim programming as provided on the second foreign convention on Multi-Objective Programming and target Programming, Torremolinos, Spain, could sixteen - 18, 1996. The ebook is an undispensable resource of the newest learn effects, offered through the best specialists of the sphere.

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Additional resources for Advances in Multiple Objective and Goal Programming: Proceedings of the Second International Conference on Multi-Objective Programming and Goal Programming, Torremolinos, Spain, May 16–18, 1996

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3, 316-319. Schell, E. ). "Application of the UNIVAC to Air Force Programming," Internal report, Headquarters, USAF, Washington, DC. Steuer, R. , 1986. Multiple Criteria Optimization, John Wiley & Sons, NY, NY. Symonds, G. , 1955. Linear Programming: The Solution of Refinery Problems, Esso Standard Oil Company, New York, New York. USAF, 1953. "The Mathematical Computation Branch: Origins, Functions, and Facilities," Internal report, DCS/Comptroller, USAF, Washington, DC. Wood, M. K. and G. B. Dantzig, 1949.

Gass, 1954. "The Parametric Objective Function Part I," Operations Research, vol. 2, no. 3, 316-319. Schell, E. ). "Application of the UNIVAC to Air Force Programming," Internal report, Headquarters, USAF, Washington, DC. Steuer, R. , 1986. Multiple Criteria Optimization, John Wiley & Sons, NY, NY. Symonds, G. , 1955. Linear Programming: The Solution of Refinery Problems, Esso Standard Oil Company, New York, New York. USAF, 1953. "The Mathematical Computation Branch: Origins, Functions, and Facilities," Internal report, DCS/Comptroller, USAF, Washington, DC.

Pattern classification, linear goal programming, fuzzy goal programming 1 Introduction Machine learning for pattern classification is one of most important subjects in artificial intelligence. Several approaches to this topic have been developed so far: in particular, statistic discrimination analysis, artificial neural network (ANN), mathematical programming approach are popular in many real fields. In ANN, the back propagation method is widely applied for learning. However, unless several parameters are decided moderately in advance, it tends to be trapped in local minima.

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