Generalization by weight-elimination with application to forecasting
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Journal of Computer and System Sciences
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Membrane Computing: An Introduction
Membrane Computing: An Introduction
Artificial Life Applications of a Class of P Systems: Abstract Rewriting Systems on Multisets
WMP '00 Proceedings of the Workshop on Multiset Processing: Multiset Processing, Mathematical, Computer Science, and Molecular Computing Points of View
Applications of Membrane Computing (Natural Computing Series)
Applications of Membrane Computing (Natural Computing Series)
Discrete solutions to differential equations by metabolic P systems
Theoretical Computer Science
The metabolic algorithm for P systems: Principles and applications
Theoretical Computer Science
MetaPlab: A Computational Framework for Metabolic P Systems
Membrane Computing
Learning regulation functions of metabolic systems by artificial neural networks
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Hybrid Functional Petri Nets as MP systems
Natural Computing: an international journal
P systems, a new computational modelling tool for systems biology
Transactions on Computational Systems Biology VI
Evolution and oscillation in p systems: applications to biological phenomena
WMC'04 Proceedings of the 5th international conference on Membrane Computing
A tutorial for competent memetic algorithms: model, taxonomy, and design issues
IEEE Transactions on Evolutionary Computation
The quantification of pollutants in drinking water by use of artificial neural networks
Natural Computing: an international journal
Tuning p systems for solving the broadcasting problem
WMC'09 Proceedings of the 10th international conference on Membrane Computing
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Metabolic P systems are an extension of P systems employed for modeling biochemical systems in a discrete and deterministic perspective. The generation of MP models from observed data of biochemical system dynamics is a hard problem which requires to solve several subproblems. Among them, flux tuners discovery aims to identify substances and parameters involved in tuning each reaction flux. In this paper we propose a new technique for discovering flux tuners by means of neural networks. This methodology, based on backpropagation with weight elimination for neural network training and on an heuristic algorithm for computing tuning indexes, has achieved encouraging results in a synthetic case study.