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Numer publikacji: 26929

Advanced recurrent networks Jordan, Elman and Real Time Recurrent Networks in practice

Advanced recurrent networks Jordan, Elman and Real Time Recurrent Networks in practice.
Monika Swiatek IPSPAN Warsaw 30.01.2015

Jordan neural networks:

Jordan neural network is used for prediction of road traffic using neural networks approach. We employ artificial neural networks for traffic furcating applied on road section. Recurrent Jorden networks popular in the modeling of time series .Jordan neural network has good generalization ability. Jordan neural network is proposed in 1986.
Technology intelligence indicates the concept and applications that transform data hidden in patents or scientific literatures into technical insight for technology strategy-making support. The existing frameworks and applications of technology intelligence mainly focus on obtaining text-based knowledge with text mining components. In order to capture the hidden trend turning points and improve the framework of existing technology intelligence, this paper proposes a patent time series processing component with trend identification functionality. We use piecewise linear representation method to generate and quantify the trend of patent publication activities, then utilize the outcome to identify trend turning points and provide trend tags to the existing text mining component, thus making it possible to combine the text-based and time-based knowledge together to support technology strategy making more satisfactorily. Also used to detect attacks against databases using SQL.
ELMAN NEURAL NETWORKS:
Elman neural network used in model predictive control non liner system. Elman neural network is capable for providing the standard state space representing for the dynamic systems. This is main reason that network architecture is utilized as recurrent neural equalizer.
There are various approaches to predictive control by AI neural networks. These methods used ANN as the planet model in order to get output prediction. For liner and non-liner processes model predictive control predictions are used. The main idea to use MPC algorithm is to use dynamic model of process for prediction the effect of future control action on the output of the process. The Elman network is normally used for object trajectory prediction, and for the generation-recognition of linguistic patterns, to detect attacks against databases using SQL(like Jordan’s NN), for the analysis of a set of commands used Unix operating system, cluster and state analys. It is characterized the context neurons receive the copy of hidden neurons of the networks and this connection do not take any associate parameters which creates an internal state of the network to exhibit temporal behavior. It is characterized the context neurons receive the copy of hidden neurons of the networks and this connection do not take any associate parameters which creates an internal state of the network to exhibit temporal behavior. Prediction of road traffic using neural networks approach. We employ artificial neural networks for traffic furcating applied on road section. Recurrent Jorden networks popular in the modelling of time series. Jordan neural network has good generalization ability.
Technology intelligence indicates the concept and applications that transform data hidden in patents or scientific literatures into technical insight for technology strategy-making support. The existing frameworks and applications of technology intelligence mainly focus on obtaining text-based knowledge with text mining components. In order to capture the hidden trend turning points and improve the framework of existing technology intelligence, this paper proposes a patent time series processing component with trend identification functionality. We use piecewise linear representation method to generate and quantify the trend of patent publication activities, then utilize the outcome to identify trend turning points and provide trend tags to the existing text mining component, thus making it possible to combine the text-based and time-based knowledge together to support technology strategy making more satisfactorily.
Elman and Jordan networks are also known as simple recurrent networks (SRN). Recurrent Neural Networks in Computer-Based Clinical Decision Support for Laryngopathies. The approach used is on the base of speech signal analysis using recurrent neural networks RNNs and RNNs can be used for patterns recognition in time series data due to memorizing ability. The Elman - Jordan networks manifest a faster and more exact achievement of target patterns. Two kind of laryngopathy followed in experiment of speech signal patterns from the control group.

RTRN

RTRN is national, NCRR funded consortium of basic behavioral, clinical and translational researcher. The RCMI translational research network RTRN facility gives innovative multi and cross disciplinary translational research to prevent, diagnose and treat a host of diseases of public health importance. Data Technology Coordinating Center DTCC fills the unique part of providing support of RCMI investigation. There are four working groups in the network. Bioinformatics and Computational Biology Biostatistics; Collaborator Technology, and Data Management.

RTRN’s Education and Training Platform enables its partnering institutions to combine and share resources to support comprehensive learning and study throughout the Network. The resource is centralized in this networks. The member from neural networks can get online services in form of written document, slides, videos or webcast or other information’s. RTRN has established a solid technological foundation to support intellectual exchange, generate innovative inter- and multi-disciplinary research and facilitate the movement of scientific advances throughout the translational research spectrum

RTRN task-specific subcommittees under the direction of the RTRN Steering Committee, leverage the intellectual resources available across the network to provide insight and direction in various areas including protocol review, education and training and the development and dissemination of study findings and results. Their efforts help to move researchers from an idea to funded project with notable outcomes and results that can impact change for the better in the communities in which the researchers work.

RTRN’s Translational Research Clusters (TRC) link clinical, biomedical and behavioral researchers with providers and community leaders into novel geographic and ethnically diverse research partnerships aimed at improving patient health outcomes. The clusters generate new ideas for research collaborations and educational activities. Currently, 10 clusters are active, cancer, cardiovascular and related diseases, community engagement, genes and environmental toxicology, HIV/AIDS, informatics, infectious and immunological diseases, neurological diseases and mental health, obesity and metabolic disease, and women’s and reproductive health.

Link do pelnej publikacji PDF wraz z rysunkiem znajduje sie na stronie:
https://put.academia.edu/MonikaSwiatek

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