Now showing items 3-12 of 12

  • An ensemble-boosting algorithm for classifying partial discharge defects in electrical assets. 

    Mas'ud, Abdullahi Abubakar; Ardila-Rey, Jorge Alfredo; Albarracin, Ricardo; Muhammad-Sukki, Firdaus (MCPI https://doi.org/10.3390/machines5030018, 2017-08-08)
    MAS'UD, A.A., ARDILA-REY, J.A., ALBARACIN, R. and MUHAMMAD-SUKKI, F. 2017. An ensemble-boosting algorithm for classifying partial discharge defects in electrical assets. Machines [online], 5(3), article ID 18. Available from: https://doi.org/10.3390/machines5030018
    This paper presents an ensemble-boosting algorithm (EBA) for classifying partial discharge (PD) patterns in the condition monitoring of insulation diagnosis applied for electrical assets. This approach presents an optimization ...
  • Evolution and devolved action: towards the evolution of systems. 

    Macleod, Christopher; McMinn, David; Reddipogu, Ann; Capanni, Niccolo Francesco; Maxwell, Grant M. (Robert Gordon University, 2001)
    MACLEOD, C., MCMINN, D., REDDIPOGU, A. B., CAPANNI, N. F. and MAXWELL, G. M., 2001. Evolution and devolved action: towards the evolution of systems. In: Appendix B of MCMINN, D. Using evolutionary artificial neural networks to design hierarchical animat nervous systems, Ph. D. thesis. Aberdeen : Robert Gordon University.
    The Artificial Neural Networks group at the Robert Gordon University has, over the last six years, built up considerable knowledge and practical experience in Evolutionary Artificial Neural Networks. This experience ...
  • The evolution of modular artificial neural networks 

    Muthuraman, Sethuraman (The Robert Gordon University School of Engineering, 2005)
    This thesis describes a novel approach to the evolution of Modular Artificial Neural Networks. Standard Evolutionary Algorithms, used in this application include: Genetic Algorithms, Evolutionary Strategies, Evolutionary ...
  • Evolutionary algorithms for real-time artificial neural network training. 

    Jagadeesan, Ananda Prasanna; Maxwell, Grant M.; Macleod, Christopher (Springer https://dx.doi.org/10.1007/11550907_12, 2005-09-01)
    JAGADEESAN, A., MAXWELL, G. and MACLEOD, C. 2005. Evolutionary algorithms for real-time artificial neural network training. Lecture notes in computer science [online], 3697, Proceedings of the 15th international conference on artifical neural networks (ICANN 2005): formal models and their applications, 11-15 September 2005, Warsaw, Poland, part 2, pages 73-78. Available from: https://dx.doi.org/10.1007/11550907_12
    This paper reports on experiments investigating the use of Evolutionary Algorithms to train Artificial Neural Networks in real time. A simulated legged mobile robot was used as a test bed in the experiments. Since the ...
  • The functionality of spatial and time domain artificial neural models 

    Capanni, Niccolo Francesco (The Robert Gordon University School of Engineering, 2006-08)
    This thesis investigates the functionality of the units used in connectionist Artificial Intelligence systems. Artificial Neural Networks form the foundation of the research and their units, Artificial Neurons, are first ...
  • Intelligent measurement in unmanned aerial cyber physical systems for traffic surveillance. 

    Petrovski, Andrei; Rattadilok, Prapa; Petrovski, Sergei (Springer https://doi.org/10.1007/978-3-319-44188-7_12, 2016-08-19)
    PETROVSKI, A., RATTADILOK, P. and PETROVSKII, S. 2016. Intelligent measurement in unmanned aerial cyber physical systems for traffic surveillance. In Jayne, C. and Iliadis, L. (eds.) Communications in computer and information science, 629. Engineering applications of neural networks: proceedings of the 17th International conference on engineering applications of neural networks (EANN 2016), 2 - 5 September 2016, Aberdeen, UK. Cham: Springer [online], pages 161-175. Available from: https://doi.org/10.1007/978-3-319-44188-7_12
    An adaptive framework for building intelligent measurement systems has been proposed in the paper and tested on simulated traffic surveillance data. The use of the framework enables making intelligent decisions related to ...
  • The synthesis of artificial neural networks using single string evolutionary techniques. 

    Macleod, Christopher (The Robert Gordon University School of Electronic and Electrical Engineering, 1999)
    The research presented in this thesis is concerned with optimising the structure of Artificial Neural Networks. These techniques are based on computer modelling of biological evolution or foetal development. They are ...
  • Temporal patterns in artificial reaction networks. 

    Gerrard, Claire E.; McCall, John; Coghill, George M.; Macleod, Christopher (Springer Verlag. http://www.springerlink.com/content/?k=(lncs+7552)+AND+(claire+gerrard), 2012-09)
    GERRARD, C., MCCALL, J., COGHILL, G. M. and MACLEOD, C., 2012. Temporal patterns in artificial reaction networks. In: A. E. P. VILLA et al (eds.) Artificial Neural Networks and Machine Learning – ICANN 2012: 22nd Annual Conference on Artificial Neural Networks. 11-14 September 2012. Berlin: Springer. Pp. 1-8.
    The Artificial Reaction Network (ARN) is a bio-inspired connection-ist paradigm based on the emerging field of Cellular Intelligence. It has proper-ties in common with both AI and Systems Biology techniques including ...
  • Using evolutionary artificial neural networks to design hierarchical animat nervous systems. 

    McMinn, David (The Robert Gordon University School of Engineering, 2001-12)
    The research presented in this thesis examines the area of control systems for robots or animats (animal-like robots). Existing systems have problems in that they require a great deal of manual design or are limited to ...
  • Using orthogonal arrays to train artificial neural networks. 

    Viswanathan, Alagappan (The Robert Gordon University School of Engineering, 2005-10)
    The thesis outlines the use of Orthogonal Arrays for the training of Artificial Neural Networks. Such arrays are popularly used in system optimisation and are known as Taguchi Methods. The chief advantage of the method is ...