IEEE Transactions on Software Engineering
Elements of distributed algorithms: modeling and analysis with Petri nets
Elements of distributed algorithms: modeling and analysis with Petri nets
Neuro Fuzzy Systems: Sate-of-the-Art Modeling Techniques
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
Modeling and Analysis of Fast Handoff Algorithms for Microcellular Networks
MASCOTS '02 Proceedings of the 10th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems
Modeling and Analysis of Random Walk Search Algorithms in P2P Networks
HOT-P2P '05 Proceedings of the Second International Workshop on Hot Topics in Peer-to-Peer Systems
Formal modeling and analysis of wireless sensor network algorithms in real-time maude
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Convergence analysis of canonical genetic algorithms
IEEE Transactions on Neural Networks
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In general, the modeling and analysis of algorithmic systems involve discrete structural elements. However, the modeling and analysis of recursive algorithmic systems can be done in the form of differential equation following control theoretic approaches. In this paper, the modeling and analysis of generalized algorithmic systems are proposed based on heuristics along with z-domain formulation in order to determine the stability of the systems. The recursive algorithmic systems are analyzed in the form of differential equation for asymptotic analysis. The biplane structure is employed for determining the boundary of the recursions, stability and, oscillatory behaviour. This paper illustrates that biplane structural model can compute the convergence of complex recursive algorithmic systems through periodic perturbation.