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.

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