TY - JOUR
T1 - Design of bio-inspired heuristic technique integrated with interior-point algorithm to analyze the dynamics of heartbeat model
AU - Raja, Muhammad Asif Zahoor
AU - Shah, Fiaz Hussain
AU - Alaidarous, Eman Salem
AU - Syam, Muhammad Ibrahim
N1 - Publisher Copyright:
© 2016 Elsevier B.V.
PY - 2017/3/1
Y1 - 2017/3/1
N2 - In this study, bio-inspired computing is presented for finding an approximate solution of governing system represents the dynamics of the HeartBeat Model (HBM) using feed-forward Artificial Neural Networks (ANNs), optimized with Genetic Algorithms (GAs) hybridized with Interiort-Point Algorithm (IPA). The modeling of the system is performed with ANNs by defining an unsupervised error function and optimization of unknown weights are carried out with GA-IPA; in which, GAs is used as an effective global search method and IPA for rapid local convergence. Design scheme is applied to study the dynamics of HBM by taking different values for perturbation factor, tension factor in the muscle fiber and the length of the muscle fiber in the diastolic state. A large number of simulations are performed for the proposed scheme to determine its effectiveness and reliability through different performance indices based on mean absolute deviation, Nash-Sutcliffe efficiency, and Thiel's inequality coefficient.
AB - In this study, bio-inspired computing is presented for finding an approximate solution of governing system represents the dynamics of the HeartBeat Model (HBM) using feed-forward Artificial Neural Networks (ANNs), optimized with Genetic Algorithms (GAs) hybridized with Interiort-Point Algorithm (IPA). The modeling of the system is performed with ANNs by defining an unsupervised error function and optimization of unknown weights are carried out with GA-IPA; in which, GAs is used as an effective global search method and IPA for rapid local convergence. Design scheme is applied to study the dynamics of HBM by taking different values for perturbation factor, tension factor in the muscle fiber and the length of the muscle fiber in the diastolic state. A large number of simulations are performed for the proposed scheme to determine its effectiveness and reliability through different performance indices based on mean absolute deviation, Nash-Sutcliffe efficiency, and Thiel's inequality coefficient.
KW - Artificial Neural Networks
KW - Genetic algorithms
KW - Heartbeat dynamics
KW - Hybrid computing
KW - Interior point methods
KW - Nonlinear systems
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U2 - 10.1016/j.asoc.2016.10.009
DO - 10.1016/j.asoc.2016.10.009
M3 - Article
AN - SCOPUS:85006713561
SN - 1568-4946
VL - 52
SP - 605
EP - 629
JO - Applied Soft Computing Journal
JF - Applied Soft Computing Journal
ER -