—The Bi-directional Evolutionary Structural Optimization scheme (BESO) is a well tested method for a wide range of topology optimization problems. In this paper a heuristic selection scheme for orthotropic materials is implemented in a 2D BESO algorithm. A method is presented to achieve convergence of both, the material orientation and material… (More)
—In this paper a heuristic optimisation technique for the maximisation of weight specific elastic deformation energy of CFRP z-frames used in aerospace applications is investigated. Therein, the focus was on the simultaneous consideration of mixed discrete and continuous variables. For that purpose a parametric finite element model was established. In that… (More)
This paper analyzes generalization of the classic Rescorla-Wagner (R-W) learning algorithm and studies their relationship to Maximum Likelihood estimation of causal parameters. We prove that the parameters of two popular causal models, ∆P and P C, can be learnt by the same generalized linear Rescorla-Wagner (GLRW) algorithm provided gener-icity conditions… (More)
This paper analyses the Contrastive Divergence algorithm for learning statistical parameters. We relate the algorithm to the stochastic approximation literature. This enables us to specify conditions under which the algorithm is guaranteed to converge to the optimal solution. This includes necessary and sufficient conditions for the solution to be unbiased.