Identification of coupled response models for ship steering and roll motion using support vector machines

Increasing cost of soliciting customers along with amplified efforts to improve the bottom-line amidst intense competition is driving the firms to rely on more cutting edge analytic methods by leveraging the knowledge of customer-base that is allowing the firms to engage better with customers by offering right product/service to right customer. Increased interest of the firms to engage better with their customers has evidently resulted into seeking answers to the key question: Why are customers likely to respond? in contrast to just seek answers for question: Who are likely to respond? This has resulted in developing propensity based response models that have become a center stage of marketing across customer life cycle. Propensity based response models are used to predict the probability of a customer or prospect responding to some offer or solicitation and also explain the drivers– why the customers are likely to respond. The output from these models will be used to segment markets, to design strategies, and to measure marketing performance. In our present paper we will use support vector machines and Logistic Regression to build propensity based response models and evaluate their performance.

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Modeling of Ship Manoeuvring Motion in 4 Degrees of Freedom Based on Support Vector Machines

Volume 9: Odd M. Faltinsen Honoring Symposium on Marine Hydrodynamics ◽

10.1115/omae2013-10806 ◽

2013 ◽

Cited By ~ 2

Author(s):

Xuegang Wang ◽

Zaojian Zou ◽

Feng Xu

Keyword(s):

Mathematical Model ◽

Support Vector Machines ◽

Degrees Of Freedom ◽

Support Vector ◽

Roll Motion ◽

Ship Manoeuvring ◽

Hydrodynamic Derivatives ◽

Vector Machines ◽

Roll Rate ◽

4 Degrees Of Freedom

Ship manoeuvring motion in waves is usually accompanied by nonlinear roll motion of large amplitude. In this paper, by taking account of the influence of the roll motion, a 4 degrees of freedom mathematical model of ship manoeuvring motion is derived. A method based on least squares support vector machines (LS-SVM) is proposed for identifying the hydrodynamic derivatives in the mathematical model by analyzing the data of surge speed, sway speed, yaw rate, roll rate, roll angle and rudder angle. To verify the identification method, 10°/10° zigzag tests are simulated for a container ship by using the hydrodynamic derivatives obtained from the roll planar motion mechanism (RPMM) test; the simulation data are used to identify the hydrodynamic derivatives, and the identification results are compared with those of RPMM test. The 10°/10° zigzag manoeuvring motion is predicted with the identified hydrodynamic derivatives. Besides, the identified model is used to predict the 20°/20° zigzag and 35° turning circle manoeuvres, and the predicted results are compared with those of simulation tests to demonstrate the generalization performance of the identified mathematical model.

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