Dynamical analysis of neural networks with time-varying delays using the LMI approach
- Authors: Lakshmanan, Shanmugam , Lim, Cheepeng , Bhatti, Asim , Gao, David , Nahavandi, Saeid
- Date: 2015
- Type: Text , Conference paper
- Relation: 22nd International Conference on Neural Information Processing, ICONIP 2015; Istanbul, Turkey; 9th-12th November 2015 Vol. 9491, p. 297-305
- Full Text: false
- Reviewed:
- Description: This study is concerned with the delay-range-dependent stability analysis for neural networks with time-varying delay and Markovian jumping parameters. The time-varying delay is assumed to lie in an interval of lower and upper bounds. The Markovian jumping parameters are introduced in delayed neural networks, which are modeled in a continuous-time along with finite-state Markov chain. Moreover, the sufficient condition is derived in terms of linear matrix inequalities based on appropriate Lyapunov-Krasovskii functionals and stochastic stability theory, which guarantees the globally asymptotic stable condition in the mean square. Finally, a numerical example is provided to validate the effectiveness of the proposed conditions. © Springer International Publishing Switzerland 2015.
Patient admission prediction using a pruned fuzzy min-max neural network with rule extraction
- Authors: Wang, Jin , Lim, Cheepeng , Creighton, Douglas , Khorsavi, Abbas , Nahavandi, Saeid , Ugon, Julien , Vamplew, Peter , Stranieri, Andrew , Martin, Laura , Freischmidt, Anton
- Date: 2015
- Type: Text , Journal article
- Relation: Neural Computing and Applications Vol. 26, no. 2 (2015), p. 277-289
- Full Text: false
- Reviewed:
- Description: A useful patient admission prediction model that helps the emergency department of a hospital admit patients efficiently is of great importance. It not only improves the care quality provided by the emergency department but also reduces waiting time of patients. This paper proposes an automatic prediction method for patient admission based on a fuzzy min–max neural network (FMM) with rules extraction. The FMM neural network forms a set of hyperboxes by learning through data samples, and the learned knowledge is used for prediction. In addition to providing predictions, decision rules are extracted from the FMM hyperboxes to provide an explanation for each prediction. In order to simplify the structure of FMM and the decision rules, an optimization method that simultaneously maximizes prediction accuracy and minimizes the number of FMM hyperboxes is proposed. Specifically, a genetic algorithm is formulated to find the optimal configuration of the decision rules. The experimental results using a large data set consisting of 450740 real patient records reveal that the proposed method achieves comparable or even better prediction accuracy than state-of-the-art classifiers with the additional ability to extract a set of explanatory rules to justify its predictions.
Video driven traffic modelling in paramics
- Authors: Zhou, Hailing , Creighton, Douglas , Lim, Cheepeng , Wei, Lei , Gao, David
- Date: 2013
- Type: Text , Conference paper
- Relation: Proceedings - UKSim 15th International Conference on Computer Modelling and Simulation, UKSim 2013 p. 525-530
- Full Text: false
- Reviewed:
- Description: With urbanization and vehicle availability, there exist many traffic problems including congestion, environmental impact and safety. In order to address these problems, we propose a video driven traffic modelling system in this paper. The system can simulate real-world traffic activities in a computer, based on traffic data recorded in videos. Video processing is employed to estimate metrics such as traffic volumes. These metrics are used to update the traffic system model, which is then simulated using the ParamicsTM traffic simulation platform. Video driven traffic modelling has widespread potential application in traffic systems, due to the convenience and reduced costs of model development and maintenance. Experiments are conducted in this paper to demonstrate the effectiveness of the proposed system. © 2013 IEEE.
- Description: 2003011214