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In this paper a method to design an observer for state and parameter estimation of sampled nonlinear systems where the sensor possesses an unsynchronized time is presented. The sensor measurements are transmitted together with the relative time stamps as a packet to the observer via a communication network. These packets are subject to random delay or might even be completely lost. Using the information...
In this paper, a new identification method for large heterogeneous spatially interconnected systems is presented. A string of different systems in state-space representation is considered. The proposed algorithm optimizes the Output-Error of the global system by using the Steepest-Descent and the Gauss-Newton methods. The main contribution of this work is that both the Jacobian and the Hessian matrix...
In the article a new approach for solving complex and highly nonlinear differential-algebraic equations (DAEs) was presented. An important kind of applications of DAE systems is modeling of biotechnological processes, which can have a very different course. An efficient solving of equations describing biotechnological industrial inlets results in better optimization of the processes and has a positive...
This paper describes SCADOPT, a highly-scalable open-source HPC framework written in C++ for solving PDE constrained optimization problems. The framework is designed to handle explicit time-stepping methods over structured grids for the approximation of time-dependent PDEs. It provides an interface to gradient-based optimization algorithms like L-BFGS-B. Derivatives needed for the optimization are...
In this paper, we propose a novel second order paradigm called optimal input normalization (OIN) to solve the problems of slow convergence and high complexity of MLP. By optimizing the non-orthogonal transformation matrix of input units in an equivalent network, OIN absorbs separate optimal learning factor for each synaptic weight as well as the threshold of hidden unit, leading to an improvement...
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