Titlebook: Bio-Inspired Systems: Computational and Ambient Intelligence; 10th International W Joan Cabestany,Francisco Sandoval,Juan M. Corchado Confe

是貪求

Adaptative Resonance Theory Fuzzy Networks Parallel Computation Using CUDA data-parallel models. In this paper parallelization of a Fuzzy ART algorithm is described and a detailed explanation of its implementation under CUDA is given. Experimental results show the algorithm runs up to 52 times faster on the GPU than on the CPU for testing and 18 times faster for training under specific conditions.

Alpha-Cells

cajole

Convergence in an Adaptive Neural Network: The Influence of Noise Inputs Correlationaptic weights. Finally, we present experimental results, with a systematic exploration of the network convergence when varying the input correlation, the initial weights and the distribution of hardware neurons to simulate the biological variability.

不遵守

血統(tǒng)

Switching Dynamics of Neural Systems in the Presence of Multiplicative Colored Noise complex time series and scale-free distributions for the escape times of the system. This finding may be of interest to understand nonlinear phenomena observed in real neural systems and to design bio-inspired artificial neural networks with convenient complex characteristics.

網(wǎng)絡(luò)添麻煩

Improving the Consistency of AHP Matrices Using a Multi-layer Perceptron-Based Modelatrix with adequate values for missing entries and, second, to improve the consistency matrix to an acceptable level. In this paper a model based on Multi-layer Perceptron (MLP) neural networks is presented. This model is capable of completing missing values in AHP pairwise matrices and improving its consistency at the same time.

esculent

Ventricle

Feature Selection in Survival Least Squares Support Vector Machines with Maximal Variation Constrainrate the ability of the maximal variation method to recover relevant variables from the given ones. A real life study concentrates on a breast cancer dataset containing clinical variables. The results indicate a better performance for the proposed method compared to Cox regression with an .. regularization scheme.

激勵(lì)

AVERT

Nonparametric Location Estimation for Probability Density Function Learningroves the estimation by integrating the information from several local clusters. Hence, a specific mixture component is learned for each local cluster. This leads to outperform other proposals where the local kernel is not as robust and/or there are no smoothing strategies, like the manifold Parzen windows.