The Multilayer Random Neural Network

Authors:
Jose Aguilar;Cristhian Molina
Affiliations:
CEMISID, Departamento de Computación, Escuela de Ingeniería de Sistemas, Universidad de Los Andes, Mérida, Venezuela;CEMISID, Departamento de Computación, Escuela de Ingeniería de Sistemas, Universidad de Los Andes, Mérida, Venezuela
Venue:
Neural Processing Letters
Year:
2013

Citing 14
Cited 0

Learning in the recurrent random neural network

Neural Computation
G-networks with multiple classes of negative and positive customers

Theoretical Computer Science
Definition of an energy function for the random neural to solve optimization problems

Neural Networks
Learning Algorithm and Retrieval Process for the Multiple Classes Random Neural Network Model

Neural Processing Letters
Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces

Journal of Global Optimization
Random Neural Network Recognition of Shaped Objects in Strong Clutter

ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Redes de Computadoras

Redes de Computadoras
Laser intensity vehicle classification system based on random neural network

Proceedings of the 43rd annual Southeast regional conference - Volume 1
Random neural networks with negative and positive signals and product form solution

Neural Computation
Random neural networks with synchronized interactions

Neural Computation
Synchronized Interactions in Spiked Neuronal Networks

The Computer Journal
The Random Neural Network

The Computer Journal
Learning in the feed-forward random neural network: A critical review

Performance Evaluation
Adaptive inertia weight particle swarm optimization

ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose in this paper an extended model of the random neural networks, whose architecture is multi-feedback. In this case, we suppose different layers where the neurons have communication with the neurons of the neighbor layers. We present its learning algorithm and its possible utilizations; specifically, we test its use in an encryption mechanism where each layer is responsible of a part of the encryption or decryption process. The multilayer random neural network is a stochastic neural model, in this way the entire proposed encryption model has that feature.