February 22, 2017

Advances in Computational Intelligence: 11th International by E. J. Palomo, E. Domínguez, R. M. Luque, J. Muñoz (auth.),

By E. J. Palomo, E. Domínguez, R. M. Luque, J. Muñoz (auth.), Joan Cabestany, Ignacio Rojas, Gonzalo Joya (eds.)

This two-volume set LNCS 6691 and 6692 constitutes the refereed complaints of the eleventh overseas Work-Conference on man made Neural Networks, IWANN 2011, held in Torremolinos-Málaga, Spain, in June 2011. The 154 revised papers have been conscientiously reviewed and chosen from 202 submissions for presentation in volumes. the second one quantity comprises seventy six papers geared up in topical sections on video and photograph processing; hybrid man made neural networks: types, algorithms and knowledge; advances in laptop studying for bioinformatics and computational biomedicine; biometric structures for human-machine interplay; facts mining in biomedicine; bio-inspired combinatorial optimization; making use of evolutionary computation and nature-inspired algorithms to formal equipment; fresh advances on fuzzy good judgment and gentle computing purposes; new advances in conception and purposes of ICA-based algorithms; organic and bio-inspired dynamical structures; and interactive and cognitive environments. The final part comprises nine papers from the overseas Workshop on clever structures for Context-Based info Fusion, ISCIF 2011, held at IWANN 2011.

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Extra resources for Advances in Computational Intelligence: 11th International Work-Conference on Artificial Neural Networks, IWANN 2011, Torremolinos-Málaga, Spain, June 8-10, 2011, Proceedings, Part II

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Niceto R. Luque, Jes´ us A. Garrido, Richard R. Carrillo, and Eduardo Ros Realistic Modeling of Large-Scale Networks: Spatio-temporal Dynamics and Long-Term Synaptic Plasticity in the Cerebellum . . . . . . . . Egidio D’Angelo and Sergio Solinas Event and Time Driven Hybrid Simulation of Spiking Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jesus A. Garrido, Richard R. Carrillo, Niceto R. Luque, and Eduardo Ros Author Index .

However, for the following frames the previous network structure is employed. So, the new representation is obtained by performing the iteration of the internal loop of the learning algorithm of the GNG, relocating the neurons and creating or removing edges. 95, αmax = 250. In Figure 2 a result of applying GNG to a 3D points from a SR4000 is shown. Fig. 2. Applying GNG to SR4000 data set 4 Applying a Feature Extraction Algorithm to Amplitude Images In this section we are going to test how good are images from this camera in order to apply a feature extraction algorithm.

Luque et al. Algorithm 1. Main steps of the tracking algorithm. Input. Time instant t and the features of the segmented objects xi (t) Output. Labelling of the segmented objects foreach Segmented object xi (t) do Compute winner neuron by means of Eq. (2); if Eq. (4) is satisfied then Create a new neuron. Initialize it; else Update the network using equation Eq. (3); end end Refresh the counter values belonging to the neurons which win a competition; Decrement all neurons counter values by one; Check out neuron counters and destroy neurons whose counter value is zero; Genetic algorithms (GAs) are applied to achieve automatically a suitable weighting of the features in the tracking process.

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