ABSTRACT
In this paper, it will be shown cooperation between Petri nets and neural networks. The approach to the formation of the structure of a neural network with the help of Petri nets model , which you can describe the algorithms and, in particular, encryption algorithms Built model in networks Petri, according to the proposed approach, is the basis for further construction neural network. The idea of an informal transformation, which makes sense from because the structure of the Petri net provides a justification for the structure of the neural network, which leads to a decrease in the number of parameters for training in the neural network (in the considered). At the same time, training is only a fine adjustment of the parameter values. Also the transformation can be explained by the fact that both Petri nets and neural networks are function description languages, with the difference that in the case of neural networks, the function being set must first be trained (or find the values of the parameters). This is discussed by the example of a simulator for encryption algorithms nets. Also, in the article Probabilistic Petri Nets and their properties are described, except for the technique of their use for system's research. 5 theorems are proved and 5 generalizing conclusions about application of languages of to a problem of Probabilistic Petri nets resolvability are made.
- Rovinelli, A., Sangid, M.D., Proudhon, H., Ludwig, W. Using machine learning and a data-driven approach to identify the small fatigue crack driving force in polycrystalline materials (2018) npj Computational Materials, 4 (1), article № 35, .Google Scholar
- Wang, Z., Liang, M., Delahaye, D. A hybrid machine learning model for short-term estimated time of arrival prediction in terminal manoeuvring area (2018) Transportation Research Part C: Emerging Technologies, 95, pp. 280-294.Google Scholar
- Mazuroski, W., Berger, J., Oliveira, R.C.L.F., Mendes, N. An artificial intelligence-based method to efficiently bring CFD to building simulation (2018) Journal of Building Performance Simulation, 11 (5), pp. 588-603.Google ScholarCross Ref
- Hawchar, L., El Soueidy, C.-P., Schoefs, F. Global kriging surrogate modeling for general time-variant reliability-based design optimization problems (2018) Structural and Multidisciplinary Optimization, 58 (3), pp. 955-968.Google ScholarDigital Library
- Liouane, Z., Lemlouma, T., Roose, P., Weis, F., Messaoud, H. An improved extreme learning machine model for the prediction of human scenarios in smart homes (2018) Applied Intelligence, 48 (8), pp. 2017-2030.Google ScholarDigital Library
- Gonzalez, R., Fiacchini, M., Iagnemma, K. Slippage prediction for off-road mobile robots via machine learning regression and proprioceptive sensing (2018) Robotics and Autonomous Systems, 105, pp. 85-93.Google Scholar
- Genevès, P., Calmant, T., Layaïda, N., Lepelley, M., Artemova, S., Bosson, J.-L. Scalable Machine Learning for Predicting At-Risk Profiles Upon Hospital Admission (2018) Big Data Research, 12, pp. 23-34..Google Scholar
- The Application of Neural Networks in Cryptography
Recommendations
Granular neural networks
Fuzzy neural networks (FNNs) and rough neural networks (RNNs) both have been hot research topics in the artificial intelligence in recent years. The former imitates the human brain in dealing with problems, the other takes advantage of rough set theory ...
Application of SFG in Learning Algorithms of Neural Networks
NICROSP '96: Proceedings of the 1996 International Workshop on Neural Networks for Identification, Control, Robotics, and Signal/Image Processing (NICROSP '96)Abstract: The paper presents the application of signal flow graphs (SFG) and adjoint flow graphs (AFG) in determination the gradient vector for feedforward neural networks. The presented approach is universal and applicable in the same form irrespective ...
Application of neural networks in short-term load forecasting
MMACTE'05: Proceedings of the 7th WSEAS International Conference on Mathematical Methods and Computational Techniques In Electrical EngineeringArtificial neural network is a computational intelligence technique that has found major applications in engineering and science. One of them is to design short-term load forecasting systems (STLF) which due to its complicated and nonlinear nature, the ...
Comments