Using adaptive routing to achieve quality of service
Performance Evaluation
A single-server G-queue in discrete-time with geometrical arrival and service process
Performance Evaluation
Laser intensity vehicle classification system based on random neural network
Proceedings of the 43rd annual Southeast regional conference - Volume 1
Multi-class pattern classification using neural networks
Pattern Recognition
Random neural networks with synchronized interactions
Neural Computation
A stochastic inventory system with postponed demands
Performance Evaluation
Performance Evaluation
Self-aware networks and quality of service
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Sensitivity versus accuracy in multiclass problems using memetic Pareto evolutionary neural networks
IEEE Transactions on Neural Networks
Multicategory nets of single-layer perceptrons: complexity and sample-size issues
IEEE Transactions on Neural Networks
Learning in the feed-forward random neural network: A critical review
Performance Evaluation
An initiative for a classified bibliography on G-networks
Performance Evaluation
Erol gelenbe's career and contributions
ISCIS'05 Proceedings of the 20th international conference on Computer and Information Sciences
Bibliography on G-networks, negative customers and applications
Mathematical and Computer Modelling: An International Journal
Modelling and analysis of gene regulatory networks based on the G-network
International Journal of Advanced Intelligence Paradigms
Multiobjective learning in the random neural network
International Journal of Advanced Intelligence Paradigms
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Spiked recurrent neural networks with "multiple classes" of signals have been recently introduced by Gelenbe and Fourneau (1999), as an extension of the recurrent spiked random neural network introduced by Gelenbe (1989). These new networks can represent interconnected neurons, which simultaneously process multiple streams of data such as the color information of images, or networks which simultaneously process streams of data from multiple sensors. This paper introduces a learning algorithm which applies both to recurrent and feedforward multiple signal class random neural networks (MCRNNs). It is based on gradient descent optimization of a cost function. The algorithm exploits the analytical properties of the MCRNN and requires the solution of a system of nC linear and nC nonlinear equations (where C is the number of signal classes and n is the number of neurons) each time the network learns a new input-output pair. Thus, the algorithm is of O([nC]3) complexity for the recurrent case, and O([nC]2) for a feedforward MCRNN. Finally, we apply this learning algorithm to color texture modeling (learning), based on learning the weights of a recurrent network directly from the color texture image. The same trained recurrent network is then used to generate a synthetic texture that imitates the original. This approach is illustrated with various synthetic and natural textures.