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Multiple neural network integration using a binary decision tree to improve the ECG signal recognition accuracy

Abstract

The paper presents a new system for ECG (ElectroCardioGraphy) signal recognition using different neural classifiers and a binary decision tree to provide one more processing stage to give the final recognition result. As the base classifiers, the three classical neural models, i.e., the MLP (Multi Layer Perceptron), modified TSK (Takagi-Sugeno-Kang) and the SVM (Support Vector Machine), will be applied. The coefficients in ECG signal decomposition using Hermite basis functions and the peak-to-peak periods of the ECG signals will be used as features for the classifiers. Numerical experiments will be performed for the recognition of different types of arrhythmia in the ECG signals taken from the MIT-BIH (Massachusetts Institute of Technology and Boston’s Beth Israel Hospital) Arrhythmia Database. The results will be compared with individual base classifiers’ performances and with other integration methods to show the high quality of the proposed solution

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Exploring complex and big data

, Inc., Red Hook, NY, pp. 1097-1105. Krawiec, K. (2016). Evolutionary feature selection and construction, in S. Claude and G. Webb (Eds.), Encyclopedia of Machine Learning and Data Mining, Springer, Boston, MA. Langegger, A., Wöß, W. and Blöchl, M. (2008). A semantic web middleware for virtual data integration on the web, European Semantic Web Conference on the Semantic Web: Research and Applications (ESWC), Tenerife, Canary Islands, Spain, pp. 493-507. LeCun, Y., Bengio, Y. and Hinton, G. (2015). Deep learning, Nature

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High-Order Variational Time Integrators for Particle Dynamics

References 1. J. E. Marsden and M. West, Discrete mechanics and variational integrators, Acta Numer., vol. 10, pp. 357{514, 2001. 2. C. Kane, J. E. Marsden, M. Ortiz, and M. West, Variational integrators and the Newmark algorithmfor conservative and dissipative mechanical systems, Internat. J. Numer. Methods Engrg., vol. 49, no. 10, pp. 1295{1325, 2000. 3. E. Hairer, C. Lubich, and G. Wanner, Geometric numerical integration, vol. 31 of Springer Series in Computational Mathematics. Springer, Heidelberg, 2010

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Reconfigurable control design with integration of a reference governor and reliability indicators

Reconfigurable control design with integration of a reference governor and reliability indicators

A new approach to manage actuator redundancy in the presence of faults is proposed based on reliability indicators and a reference governor. The aim is to preserve the health of the actuators and the availability of the system both in the nominal behavior and in the presence of actuator faults. The use of reference governor control allocation is a solution to distribute the control efforts among a redundant set of actuators. In a degraded situation, a reconfigured control allocation strategy is proposed based on on-line re-estimation of the actuator reliability. A benefit of incorporating reliability indicators into over-actuated control system design is the smart management of the redundant actuators and improvement of the system safety. Moreover, when the fault is severe, an adaptation approach using the reference governor is proposed. The reference governor unit is a reference-offset governor based on a discrete-time predictive control strategy. The idea is to modify the reference according to the system constraints, which become stricter after the occurrence of an actuator fault. The proposed approach is illustrated with a flight control application.

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The Phase–Space Approach to time Evolution of Quantum States in Confined Systems: the Spectral Split–Operator Method

–1–052108–6, DOI: 10.1103/PhysRevA.88.052108. Castellani, L. (2000). Non-commutative geometry and physics: A review of selected recent results, Classical and Quantum Gravity 17 (17): 3377–3401, DOI: 10.1088/0264-9381/17/17/301. Chin, S.A. (1997). Symplectic integrators from composite operator factorizations, Physics Letters A 226 (6): 344–348, DOI: 10.1016/S0375-9601(97)00003-0. Chin, S.A. and Chen, C.R. (2002). Gradient symplectic algorithms for solving the Schrödinger equation with time-dependent potentials, The Journal of Chemical Physics 117 (4

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A Three–Level Aggregation Model for Evaluating Software Usability by Fuzzy Logic

., Bolko, I., Giesen, D., Gravem, D. and Haraldsen, G. (2011). Final report integrating findings on business perspectives related to NSIS’ statistics, Technical report , Deliverable 3.2., FP7 Blue-Ets Project, European Commission, Brussels, https://cordis.europa.eu/project/rcn/94081/results/en?rcn=143042 . Beliakov, G., Pradera, A. and Calvo, T. (2007). Aggregation Functions: A Guide for Practitioners , Springer-Verlag, Berlin/Heidelberg. Calinescu, M. and Schouten, B. (2012). Adaptive survey designs that minimize nonresponse and measurement risk

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Centers: their integrability and relations with the divergence

) , $$\begin{array}{} \displaystyle \begin{array}{*{20}{l}} {\dot x = {X_2}(x,y),}\\ {\dot y = {Y_2}(x,y),} \end{array} \end{array}$$ (4) called a degenerate center , where X 2 ( x , y ) and Y 2 ( x , y ) are real analytic functions without constant and linear terms, defined in a neighborhood of the origin. 3 About the integrability of the centers The characterization of linear type centers using their first integrals is due to Poincaré [ 22 ] in the case of polynomial differential systems and to Liapunov [ 16 ] in the case of analytic differential systems, see

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An operational Haar wavelet method for solving fractional Volterra integral equations

Astronautics Journal 23(6): 918-925. Baillie, R.T. (1996). Long memory processes and fractional integration in econometrics, Journal of Econometrics 73(1): 5-59. Baratella, P. and Orsi, A.P. (2004). New approach to the numerical solution of weakly singular Volterra integral equations, Journal of Computational and Applied Mathematics 163(2): 401-418. Brunner, H. (1984). The numerical solution of integral equations with weakly singular kernels, in D.F. GriMths (Ed.), Numerical Analysis

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On-line parameter and delay estimation of continuous-time dynamic systems

References Chao, Y.C., Chen, C.L. and Huang, H.P. (1987). Recursive parameter estimation of transfer function matrix models via Simpsonâ˘A ´ Zs integrating rules, International Journal of Systems Science 18(5): 901-911. Ferretti, G., Maffezzoni, C. and Scattolini, R. (1991). Recursive estimation of time delay in sampled systems, Automatica 27(4): 653-661. Goldberg, D.E. (1989). Genetic Algorithms in Search, Opimization and Machine Learning, Addison-Wesley, Reading, MA. Ikonen, E., Najim, K. and

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Reliability–based economic model predictive control for generalised flow–based networks including actuators’ health–aware capabilities

-Cerrillo, J. and Megan, L. (2013). Integration of control theory and scheduling methods for supply chain management, Computers & Chemical Engineering 51 (0): 4–20. Weber, P., Boussaid, B., Khelassi, A., Theilliol, D. and Aubrun, C. (2012). Reconfigurable control design with integration of a reference governor and reliability indicators, International Journal of Applied Mathematics and Computer Science 22 (1): 139–148, DOI: 10.2478/v10006-012-0010-0.

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