Coyle, DH, Prasad, G and McGinnity, TM (2010) Faster Self-Organizing Fuzzy Neural Network Training and Improved Autonomy withTime-Delayed Synapses for Locally Recurrent Learning. In: System and Circuit Design for Biologically-Inspired Intelligent Learning. Medical Information Science Reference (an imprint of IGI Global), pp. 156-183. ISBN ISBN 978-1-60960-018-1 (hardcover) [Book section]
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This chapter describes a number of modifications to the learning algorithm and architecture of the selforganizing fuzzy neural network (SOFNN) to improve its computational efficiency and learning ability. To improve the SOFNN’s computational efficiency, a new method of checking the network structure after it has been modified is proposed. Instead of testing the entire structure every time it has been modified, a record is kept of each neuron’s firing strength for all data previously clustered by the network. This record is updated as training progresses and is used to reduce the computational load of checking networkstructure changes, to ensure performance degradation does not occur, resulting in significantly reduced training times. It is shown that the modified SOFNN compares favorably to other evolving fuzzy systems in terms of accuracy and structural complexity. In addition, a new architecture of the SOFNN is proposed where recurrent feedback connections are added to neurons in layer three of the structure. Recurrent connections allow the network to learn the temporal information from data and, in contrast to pure feed forward architectures which exhibit static input-output behavior in advance, recurrent models are able to store information from the past (e.g., past measurements of the time-series) and are thereforebetter suited to analyzing dynamic systems. Each recurrent feedback connection includes a weight which must be learned. In this work a learning approach is proposed where the recurrent feedback weight isupdated online (not iteratively) and proportional to the aggregate firing activity of each fuzzy neuron. It is shown that this modification can significantly improve the performance of the SOFNN’s prediction capacity under certain constraints.
|Item Type:||Book section|
|Faculties and Schools:||Faculty of Computing & Engineering|
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
|Research Institutes and Groups:||Computer Science Research Institute|
Computer Science Research Institute > Intelligent Systems Research Centre
|Deposited By:||Professor Martin McGinnity|
|Deposited On:||10 Dec 2010 12:58|
|Last Modified:||10 Dec 2010 12:58|
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