205 research outputs found
The performance of approximating ordinary differential equations by neural nets
No full textThe dynamics of many systems are described by ordinary differential equations (ODE). Solving ODEs with standard methods (i.e. numerical integration) needs a high amount of computing time but only a small amount of storage memory. For some applications, e.g. short time weather forecast or real time robot control, long computation times are prohibitive. Is there a method which uses less computing time (but has drawbacks in other aspects, e.g. memory), so that the computation of ODEs gets faster? We will try to discuss this question for the assumption that the alternative computation method is a neural network which was trained on ODE dynamics and compare both methods using the same approximation error. This comparison is done with two different errors. First, we use the standard error that measures the difference between the approximation and the solution of the ODE which is hard to characterize. But in many cases, as for physics engines used in computer games, the shape of the approximation curve is important and not the exact values of the approximation. Therefore, we introduce a subjective error based on the Total Least Square Error (TLSE) which gives more consistent results. For the final performance comparison, we calculate the optimal resource usage for the neural network and evaluate it depending on the resolution of the interpolation points and the inter-point distance. Our conclusion gives a method to evaluate where neural nets are advantageous over numerical ODE integration and where this is not the case. Index Terms—ODE, neural nets, Euler method, approximation complexity, storage optimization
Der Königsweg zum Herzen: der Einfluss nichtmedizinischer Merkmale auf die Versorgung mit invasiven kardiologischen Leistungen
No full textBrause M. Der Königsweg zum Herzen: der Einfluss nichtmedizinischer Merkmale auf die Versorgung mit invasiven kardiologischen Leistungen. Verlag Hans Huber: Programmbereich Gesundheit. Bern: Huber; 2009
Gesundheitsförderung in der stationären Langzeitversorgung. Ergebnisse einer Expertenbefragung
No full textHorn A, Brause M, Schaeffer D. Gesundheitsförderung in der stationären Langzeitversorgung. Ergebnisse einer Expertenbefragung. Prävention und Gesundheitsförderung. 2011;6(4):262-269
A VLSI-design of the minimum entropy neuron
No full textOne of the most interesting domains of feedforward networks is the processing of sensor signals. There do exist some networks which extract most of the information by implementing the maximum entropy principle for Gaussian sources. This is done by transforming input patterns to the base of eigenvectors of the input autocorrelation matrix with the biggest eigenvalues. The basic building block of these networks is the linear neuron, learning with the Oja learning rule. Nevertheless, some researchers in pattern recognition theory claim that for pattern recognition and classification clustering transformations are needed which reduce the intra-class entropy. This leads to stable, reliable features and is implemented for Gaussian sources by a linear transformation using the eigenvectors with the smallest eigenvalues. In another paper (Brause 1992) it is shown that the basic building block for such a transformation can be implemented by a linear neuron using an Anti-Hebb rule and restricted weights. This paper shows the analog VLSI design for such a building block, using standard modules of multiplication and addition. The most tedious problem in this VLSI-application is the design of an analog vector normalization circuitry. It can be shown that the standard approaches of weight summation will not give the convergence to the eigenvectors for a proper feature transformation. To avoid this problem, our design differs significantly from the standard approaches by computing the real Euclidean norm. Keywords: minimum entropy, principal component analysis, VLSI, neural networks, surface approximation, cluster transformation, weight normalization circuit
Gesundheits- und Arbeitssituation von Pflegenden in der stationären Langzeitversorgung
No full textBrause M, Kleina T, Cichocki M, Horn A. Gesundheits- und Arbeitssituation von Pflegenden in der stationären Langzeitversorgung. Pflege & Gesellschaft. 2013;18(1):19-34
Frauen mit chronischen Erkrankungen - Anforderungen an die Versorgungsgestaltung
No full textSchaeffer D. Frauen mit chronischen Erkrankungen - Anforderungen an die Versorgungsgestaltung. In: Tiesmeyer K, Brause M, Lukas-Nülle M, Lierse M, eds. Der blinde Fleck. Ungleichheiten in der Gesundheitsversorgung. Bern: Huber; 2008: 359-373
Erfolgreiche Rehabilitation bei Menschen mit Migrationshintergrund
No full textSchott T, Brause M, Razum O, Reutin B, Yilmaz-Aslan Y. Erfolgreiche Rehabilitation bei Menschen mit Migrationshintergrund. Public Health Forum. 2011;19(1):20.e1-20.e3
[Health promotion in long-term inpatient care: possibilities and opportunities]
No full textBrause M, Horn A, Büscher A, Schaeffer D. [Health promotion in long-term inpatient care: possibilities and opportunities]. Pflege Z. 2010;63(1):8-10
Möglichkeiten und Chancen. Gesundheitsförderung in der stationären Langzeitversorgung
No full textBrause M, Horn A, Büscher A, Schaeffer D. Möglichkeiten und Chancen. Gesundheitsförderung in der stationären Langzeitversorgung. Pflegezeitschrift. 2010;63(1):8-10
Burnout-Risiko in der stationären Langzeitversorgung. Ressourcen und Belastungen von Pflege- und Betreuungskräften
No full textBrause M, Kleina T, Horn A, Schaeffer D. Burnout-Risiko in der stationären Langzeitversorgung. Ressourcen und Belastungen von Pflege- und Betreuungskräften. Prävention und Gesundheitsförderung. 2015;10(1):41-48
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