Search results for "Computation"

Increasing sample efficiency in deep reinforcement learning using generative environment modelling

2020

Identification of the Parameters of Reduced Vector Preisach Model by Neural Networks

2008

This paper presents a methodology for identifying reduced vector Preisach model parameters by using neural networks. The neural network used is a multiplayer perceptron trained with the Levenberg-Marquadt training algorithm. The network is trained by some hysteresis data, which are generated by using reduced vector Preisach model with preassigned parameters. It is shown how a properly trained network is able to find the parameters needed to best fit a magnetization hysteresis curve.

An Adaptive Global-Local Memetic Algorithm to Discover Resources in P2P Networks

2007

This paper proposes a neural network based approach for solving the resource discovery problem in Peer to Peer (P2P) networks and an Adaptive Global Local Memetic Algorithm (AGLMA) for performing the training of the neural network. This training is very challenging due to the large number of weights and noise caused by the dynamic neural network testing. The AGLMA is a memetic algorithm consisting of an evolutionary framework which adaptively employs two local searchers having different exploration logic and pivot rules. Furthermore, the AGLMA makes an adaptive noise compensation by means of explicit averaging on the fitness values and a dynamic population sizing which aims to follow the ne…

Problems of coding stereo images in human memory

2010

This paper discusses the memorization and recall by man of a sequence of planar or stereoscopic images, including six frames that contain a planar strip (8×8 positions of the stimulus) or a volume strip (8×4×2 positions). At the recall stage, the subject chose between the stimulus and three distractors in each frame. It is shown that the times for recognition and recall are less for volume stimuli, while the percent of correct responses is greater for planar stimuli. For volume stimuli, the distribution of errors depends on the disparity between the target and the selected distractor. A model based on a heteroassociative neural network reproduces the error distribution for planar but not fo…

A spiking network for body size learning inspired by the fruit fly

2013

The concept of peripersonal space is an interesting research topics for psychologists, neurobiologists and for robotic applications. A living being can learn the representation of its own body to take the correct behavioral decision when interacting with the world. To transfer these important learning mechanisms on bio-robots, simple and efficient solutions can be found in the insect world. In this paper a neural-based model for body-size learning is proposed taking into account the results obtained in experiments with fruit flies. Simulations and experimental results on a roving platform are reported and compared with the biological counterpart.

Regularized RBF Networks for Hyperspectral Data Classification

2004

In this paper, we analyze several regularized types of Radial Basis Function (RBF) Networks for crop classification using hyperspectral images. We compare the regularized RBF neural network with Support Vector Machines (SVM) using the RBF kernel, and AdaBoost Regularized (ABR) algorithm using RBF bases, in terms of accuracy and robustness. Several scenarios of increasing input space dimensionality are tested for six images containing six crop classes. Also, regularization, sparseness, and knowledge extraction are paid attention.

Why Cortices? Neural Networks for Visual Information Processing

1989

Neural networks for the processing of sensory information show remarkable similarities between different species and across different sensory modalities. As an example, cortical organization found in the mamalian neopallium and in the optic tecta of most vertebrates appears to be equally appropriate as a substrate for visual, auditory, and somatosensory information processing. In this paper, we formulate three structural principles of the vertebrate visual cortex that allow to analyze structure and function of these neural networks on an intermediate level of complexity. Computational applications are taken from the field of early vision. The proposed principles are: (a) Average anatomy, i …

A Memetic-Neural Approach to Discover Resources in P2P Networks

2008

This chapter proposes a neural network based approach for solving the resource discovery problem in Peer to Peer (P2P) networks and an Adaptive Global Local Memetic Algorithm (AGLMA) for performing in training of the neural network. The neural network, which is a multi-layer perceptron neural network, allows the P2P nodes to efficiently locate resources desired by the user. The necessity of testing the network in various working conditions, aiming to obtain a robust neural network, introduces noise in the objective function. The AGLMA is a memetic algorithm which employs two local search algorithms adaptively activated by an evolutionary framework. These local searchers, having different fe…

Estimation of Granger causality through Artificial Neural Networks: applications to physiological systems and chaotic electronic oscillators

2021

One of the most challenging problems in the study of complex dynamical systems is to find the statistical interdependencies among the system components. Granger causality (GC) represents one of the most employed approaches, based on modeling the system dynamics with a linear vector autoregressive (VAR) model and on evaluating the information flow between two processes in terms of prediction error variances. In its most advanced setting, GC analysis is performed through a state-space (SS) representation of the VAR model that allows to compute both conditional and unconditional forms of GC by solving only one regression problem. While this problem is typically solved through Ordinary Least Sq…

In silico and in vitro comparative analysis to select, validate and test SNPs for human identification.

2007

Abstract Background The recent advances in human genetics have recently provided new insights into phenotypic variation and genome variability. Current forensic DNA techniques involve the search for genetic similarities and differences between biological samples. Consequently the selection of ideal genomic biomarkers for human identification is crucial in order to ensure the highest stability and reproducibility of results. Results In the present study, we selected and validated 24 SNPs which are useful in human identification in 1,040 unrelated samples originating from three different populations (Italian, Benin Gulf and Mongolian). A Rigorous in silico selection of these markers provided …