Summary
A general neural network model for rewriting logic is proposed. This model, in the form of a feedforward multilayer net, is represented in rewriting logic along the lines of several models of parallelism and concurrency that have already been mapped into it. By combining both a right choice for the representation operations and the availability of strategies to guide the application of our rules, a new approach for the classical backpropagation learning algorithm is obtained. An example, the diagnosis of glaucoma by using campimetric fields and nerve fibres of the retina, is presented to illustrate the performance and applicability of the proposed model.
Research supported by Spanish project DESAFIOS TIN2006–15660–C02–01 and by Comunidad de Madrid program PROMESAS S–0505/TIC/0407.
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Santos-García, G., Palomino, M., Verdejo, A. (2009). Rewriting Logic Using Strategies for Neural Networks: An Implementation in Maude. In: Corchado, J.M., Rodríguez, S., Llinas, J., Molina, J.M. (eds) International Symposium on Distributed Computing and Artificial Intelligence 2008 (DCAI 2008). Advances in Soft Computing, vol 50. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85863-8_50
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DOI: https://doi.org/10.1007/978-3-540-85863-8_50
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