| Document Date: 2014-02-08 06:27:49
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City London / / Company World Scientific / matrix Rt . / / Country United Kingdom / / Currency pence / / / Event Product Issues / / IndustryTerm neural network training algorithm / cases neural networks / unconstrained neural network / continuous time neural networks / estimated neural network / neural network / time recurrent networks / recurrent neural networks / symmetric solutions / recurrent networks / symmetric solution / non-symmetric recurrent neural network / feedforward neural network / augmented back-propagation algorithm / neural networks / final solution / constrained and unconstrained neural networks / recurrent network / recurrent neural network / time recurrent network / desired symmetric solution / constrained neural network / deterministic systems / linear systems / learning algorithm / nonsymmetric recurrent neural networks / / Organization London Business School / U.S. Securities and Exchange Commission / Department of Decision Science / / Person Bolland / JEROME T. CONNOR / T. Connor Controllability / Butlin / Wang / Blum / / Position Rt / / Product data / distribution / normal distributions / / ProgrammingLanguage Scala / / Technology learning algorithm / slightly augmented back-propagation algorithm / NEURAL NETWORK / neural network system / neural network training algorithm / Data Mining / /
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International Journal of Neural Systems, Vol. 8, No. 4 (August, –415 Special Issue on Data Mining in Finance c World Scientific Publishing Company A CONSTRAINED NEURAL NETWORK KALMAN FILTER