International Journal of Computational Intelligence Systems

Volume 2, Issue 2, June 2009

Special issue on Computational Intelligence Applied to Nonlinear Dynamics and Synchronization

1. The Influence of the Update Dynamics on the Evolution of the Cooperation

Carlos Grilo, Luis Correia
Pages: 104 - 114
We investigate the influence of the update dynamics on the evolution of cooperation. Three of the most studied games in this area are used: Prisoner’s Dilemma, Snowdrift and the Stag Hunt. Previous studies with the Prisoner’s Dilemma game reported that less cooperators survive with the asynchronous...

2. Optimal IP Assignment for Efficient NoC-based System Implementation using NSGA-II and MicroGA

Marcus Vinicius Carvalho da Silva, Nadia Nedjah, Luiza de Macedo Mourelle
Pages: 115 - 123
Network-on-chip (NoC) are considered the next generation of communication infrastructure, which will be omnipresent in most of industry, office and personal electronic systems. In platform-based methodology, an application is implemented by a set of collaborating intellectual properties (IPs) blocks....

3. Cellular Neural Networks-Based Genetic Algorithm for Optimizing the Behavior of an Unstructured Robot

Alireza Fasih, Jean Chamberlain Chedjou, Kyandoghere Kyamakya
Pages: 124 - 131
A new learning algorithm for advanced robot locomotion is presented in this paper. This method involves both Cellular Neural Networks (CNN) technology and an evolutionary process based on genetic algorithm (GA) for a learning process. Learning is formulated as an optimization problem. CNN Templates...

4. Global Approximations to Cost and Production Functions using Artificial Neural Networks

Efthymios G. Tsionas, Panayotis G. Michaelides, Angelos T. Vouldis
Pages: 132 - 139
The estimation of cost and production functions in economics relies on standard specifications which are less than satisfactory in numerous situations. However, instead of fitting the data with a pre-specified model, Artificial Neural Networks (ANNs) let the data itself serve as evidence to support...

5. Networks of Mixed Canonical-Dissipative Systems and Dynamic Hebbian Learning

Julio Rodriguez, Max-Olivier Hongler
Pages: 140 - 146
We study the dynamics of a network consisting of N diffusively coupled, stable-limit-cycle oscillators on which individual frequencies are parametrized by ωk , k = 1, . . . , N. We introduce a learning rule which influences the ωk by driving the system towards a consensual oscillatory state in...

6. Radial Basis Function Nets for Time Series Prediction

Abdelhamid Bouchachia
Pages: 147 - 157
This paper introduces a novel ensemble learning approach based on recurrent radial basis function networks (RRBFN) for time series prediction with the aim of increasing the prediction accuracy. Standing for the base learner in this ensemble, the adaptive recurrent network proposed is based on the...

7. Feature Extraction for the Prognosis of Electromechanical Faults in Electrical Machines through the DWT

J.A. Antonino-Daviu, M. Riera-Guasp, M. Pineda-Sanchez, J. Pons-Llinares, R. Puche-Panadero, J. Perez-Cruz
Pages: 158 - 167
Recognition of characteristic patterns is proposed in this paper in order to diagnose the presence of electromechanical faults in induction electrical machines. Two common faults are considered; broken rotor bars and mixed eccentricities. The presence of these faults leads to the appearance of frequency...