Ambiguity in the inputs of the models is typical especially in portfolio selection problem where the true distribution of random variables is usually unknown. Here we use robust optimization approach to address the ambiguity in conditional-value-at-risk minimization model. We obtain explicit models of the robust conditional-value-at-risk minimization for polyhedral and correlated polyhedral am...

Neurons process information via integration of synaptic inputs from dendrites. Many experimental results demonstrate dendritic integration could be highly nonlinear, yet few theoretical analyses have been performed to obtain a precise quantitative characterization analytically. Based on asymptotic analysis of a two-compartment passive cable model, given a pair of time-dependent synaptic conduct...

GABAergic neurons in the caudal ventrolateral medulla (CVLM) are driven by baroreceptor inputs relayed via the nucleus tractus solitarius (NTS), and they inhibit neurons in rostral ventrolateral medulla to reduce SNA and AP. After arterial baroreceptor denervation or lesions of the NTS, inhibition of the CVLM continues to increase AP, suggesting additional inputs also tonically activate the CVL...

The control problem of soft-landing a toy lunar module simulation is investigated in the context of neural nets. While traditional supervised back-propagation training is inappropriate for lack of training exemplars, genetic algorithms allow a controller to be evolved without diiculty: Evolution is a form of unsupervised learning. A novelty introduced in this paper is the presentation of additi...

We present a method for optimizing inputs of multibody systems for a subsequently performed parameter identification. Herein, optimality with respect to identifiability is attained by maximizing the information content in measurements described by the Fisher information matrix. For solving the resulting optimization problem, the adjoint system of the sensitivity differential equation system is ...