Search results for "Neural"
showing 10 items of 2783 documents
An ant colony optimization-based fuzzy predictive control approach for nonlinear processes
2015
In this paper, a new approach for designing an adaptive fuzzy model predictive control (AFMPC) based on the ant colony optimization (ACO) is proposed. On-line adaptive fuzzy identification is introduced to identify the system parameters. These parameters are used to calculate the objective function based on a predictive approach and structure of RST control. Then the optimization problem is solved based on an ACO algorithm, used at the optimization process in AFMPC to determine optimal controller parameters of RST control. The utility of the proposed controller is demonstrated by applying it to two nonlinear processes, where the proposed approach provides better performances compared with p…
An approach based on the Adaptive Resonance Theory for analysing the viability of recommender systems in a citizen Web portal
2007
This paper proposes a methodology to optimise the future accuracy of a collaborative recommender application in a citizen Web portal. There are four stages namely, user modelling, benchmarking of clustering algorithms, prediction analysis and recommendation. The first stage is to develop analytical models of common characteristics of Web-user data. These artificial data sets are then used to evaluate the performance of clustering algorithms, in particular benchmarking the ART2 neural network with K-means clustering. Afterwards, it is evaluated the predictive accuracy of the clusters applied to a real-world data set derived from access logs to the citizen Web portal Infoville XXI (http://www…
Bidirected Information Flow in the High-Level Visual Cortex
2021
Understanding the brain function requires investigating information transfer across brain regions. Shannon began the remarkable new field of information theory in 1948. It basically can be divided into two categories: directed and undirected information-theoretical approaches. As we all know, neural signals are typically nonlinear and directed flow between brain regions. We can use directed information to quantify feed-forward information flow, feedback information, and instantaneous influence in the high-level visual cortex. Moreover, neural signals have bidirectional information flow properties and are not captured by the transfer entropy approach. Therefore, we used directed information …
Machine Learning-Based View Synthesis in Fourier Lightfield Microscopy
2022
Current interest in Fourier lightfield microscopy is increasing, due to its ability to acquire 3D images of thick dynamic samples. This technique is based on simultaneously capturing, in a single shot, and with a monocular setup, a number of orthographic perspective views of 3D microscopic samples. An essential feature of Fourier lightfield microscopy is that the number of acquired views is low, due to the trade-off relationship existing between the number of views and their corresponding lateral resolution. Therefore, it is important to have a tool for the generation of a high number of synthesized view images, without compromising their lateral resolution. In this context we investigate h…
Neural Networks to Determine the Relationships Between Business Innovation and Gender Aspects
2021
Gender aspects of management, innovation and entrepreneurship are gaining more and more importance as cross-cutting issues for researchers, practitioners and decision makers. Extant literature pays a growing attention to the hypothesis that there exists a correlation between the gender diversity of corporate boards of directors and the business attitude to innovation. In this paper we introduce a working framework to test the aforementioned hypothesis and to examine the correlation between board diversity and innovation perception of a business. This framework is based on correlation computation and feed-forward neural networks, and it is used to evaluate whether the gender component may be…
Slow and fast methyl group rotations in fragile glass-formers studied by NMR
2000
Abstract The spin-lattice relaxation times of the selectively ring deuterated, fragile glass-formers propylene carbonate and toluene were compared with those measured for species which were specifically labeled at the methyl groups. It was found that the dynamics of the CD 3 group is strongly decoupled from that associated with the primary response of toluene, while for propylene carbonate the degree of decoupling is relatively weak. The experimental results could be described successfully using a model which takes into account the ring dynamics as well as those of the methyl group.
Neural networks as effective techniques in clinical management of patients: some case studies
2004
In this paper, we present four examples of effective implementation of neural systems in the daily clinical practice. There are two main goals in this work; the first one is to show that neural networks are especially well-suited tools for solving different kind of medical/pharmaceutical problems, given the complex input output relationships and the few a priori knowledge about data distribution and variable relations. The second goal is to develop specific software applications, which enclose complex mathematical models, to clinicians; thus, the use of such models as decision support systems is facilitated. Four important pharmaceutical problems are considered in this study: identificatio…
Modelling the insect Mushroom Bodies: Application to sequence learning
2015
Learning and reproducing temporal sequences is a fundamental ability used by living beings to adapt behaviour repertoire to environmental constraints. This paper is focused on the description of a model based on spiking neurons, able to learn and autonomously generate a sequence of events. The neural architecture is inspired by the insect Mushroom Bodies (MBs) that are a crucial centre for multimodal sensory integration and behaviour modulation. The sequence learning capability coexists, within the insect brain computational model, with all the other features already addressed like attention, expectation, learning classification and others. This is a clear example that a unique neural struc…
Architectural improvements and FPGA implementation of a multimodel neuroprocessor
2003
Since neural networks (NNs) require an enormous amount of learning time, various kinds of dedicated parallel computers have been developed. In the paper a 2-D systolic array (SA) of dedicated processing elements (PEs) also called systolic cells (SCs) is presented as the heart of a multimodel neural-network accelerator. The instruction set of the SA allows the implementation of several neural algorithms, including error back propagation and a self organizing feature map algorithm. Several special architectural facilities are presented in the paper in order to improve the 2-D SA performance. A swapping mechanism of the weight matrix allows the implementation of NNs larger than 2-D SA. A systo…
Neurological deficits like CVA, Trigeminal neuralgia, Post herpetic neuralgia, Meningitis, Cervical Myelopathy, Demyelinating Disease, Acoustic Neuro…
2018
Patients and methods. A total number of 35 patients with neurological deficits, like cerebrovascular accident (CVA), trigeminal neuralgia, post herpetic neuralgia, meningitis, cervical myelopathy, demyelinating disease, acoustic neuroma, muscular neuropathy and parkinsonism were treated with ozone therapy in our institution. Results. Group 1) Hemorrhagic - 6 patients Group 2) Ischemic - 29 Patients Both the groups showed improvement varying from 20% to 100%: • Hemorrhagic patients took more time for improvement as compare to ischemic group. • Hemorrhagic patients showed partial recovery. • Ischemic group showed almost 90% - 100% recovery. Patients who received ozone therapy after 12 months …