A New Approach to Knowledge-Based Design of Recurrent Neural Networks

The article considers the problem of forecasting nonlinear nonstationary processes, presented in the form of time series, which can describe the dynamics of processes in both technical and economic systems. The general technique of analysis of such data and construction of corresponding mathematical models based on autoregressive models and recurrent neural networks is described in detail. The technique is applied on practical examples while performing the comparative analysis of models of forecasting of quantity of channels of service of cellular subscribers for a given station and revealing advantages and disadvantages of each method. The need to improve the existing methodology and develop a new approach is formulated.

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Towards an Embedding-Based Approach for the Geolocation of Texts and Users on Social Networks

Interdisciplinary Approaches to Spatial Optimization Issues - Advances in Geospatial Technologies ◽

10.4018/978-1-7998-1954-7.ch012 ◽

2021 ◽

pp. 206-234

Author(s):

Sarra Hasni

Keyword(s):

Neural Networks ◽

Social Networks ◽

Spatial Analysis ◽

Recurrent Neural Networks ◽

Contextual Information ◽

Language Models ◽

New Approach ◽

Textual Data ◽

Word Forms ◽

Statistical Language Models

The geolocation task of textual data shared on social networks like Twitter attracts a progressive attention. Since those data are supported by advanced geographic information systems for multipurpose spatial analysis, new trends to extend the paradigm of geolocated data become more emergent. Differently from statistical language models that are widely adopted in prior works, the authors propose a new approach that is adopted to the geolocation of both tweets and users through the application of embedding models. The authors boost the geolocation strategy with a sequential modelling using recurrent neural networks to delimit the importance of words in tweets with respect to contextual information. They evaluate the power of this strategy in order to determine locations of unstructured texts that reflect unlimited user's writing styles. Especially, the authors demonstrate that semantic proprieties and word forms can be effective to geolocate texts without specifying local words or topics' descriptions per region.

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Control of Recurrent Neural Networks Using Differential Minimax Game: The Stochastic Case

ASME 2010 Dynamic Systems and Control Conference, Volume 2 ◽

10.1115/dscc2010-4006 ◽

2010 ◽

Author(s):

Ziqian Liu ◽

Nirwan Ansari

Keyword(s):

Neural Networks ◽

Recurrent Neural Networks ◽

New Approach ◽

Numerical Example ◽

Inverse Optimality ◽

Stochastic Case ◽

Stochastic Input ◽

Minimax Game ◽

Unknown Covariance ◽

Lyapunov Technique

As a continuation of our study, this paper extends our research results of optimality-oriented stabilization from deterministic recurrent neural networks to stochastic recurrent neural networks, and presents a new approach to achieve optimally stochastic input-to-state stabilization in probability for stochastic recurrent neural networks driven by noise of unknown covariance. This approach is developed by using stochastic differential minimax game, Hamilton-Jacobi-Isaacs (HJI) equation, inverse optimality, and Lyapunov technique. A numerical example is given to demonstrate the effectiveness of the proposed approach.

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Gene Expression Analysis using Markov Chains extracted from RNNs

CLEI electronic journal ◽

10.19153/cleiej.10.2.8 ◽

2007 ◽

Vol 10 (2) ◽

Author(s):

Igor Lorenzato Almeida ◽

Denise Regina Pechmann ◽

Adelmo Luis Cechin

Keyword(s):

Gene Expression ◽

Neural Networks ◽

Markov Chains ◽

Microarray Data ◽

Recurrent Neural Networks ◽

Gene Expression Analysis ◽

Data Sets ◽

New Approach ◽

Several Variables ◽

Gene Expression Levels

This paper present a new approach for the analysis of gene expres- sion, by extracting a Markov Chain from trained Recurrent Neural Networks (RNNs). A lot of microarray data is being generated, since array technologies have been widely used to monitor simultaneously the expression pattern of thou- sands of genes. Microarray data is highly specialized, involves several variables in which are complex to express and analyze. The challenge is to discover how to extract useful information from these data sets. So this work proposes the use of RNNs for data modeling, due to their ability to learn complex temporal non-linear data. Once a model is obtained for the data, it is possible to ex- tract the acquired knowledge and to represent it through Markov Chains model. Markov Chains are easily visualized in the form of states graphs, which show the influences among the gene expression levels and their changes in time

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On the way to fault detection method in moving load dynamics problem by modified recurrent neural networks approach

Mechanics & Industry ◽

10.1051/meca/2021009 ◽

2021 ◽

Vol 22 ◽

pp. 10

Author(s):

Shakti P. Jena ◽

Dayal R. Parhi

Keyword(s):

Neural Networks ◽

Recurrent Neural Networks ◽

Detection Method ◽

Moving Load ◽

Back Propagation ◽

Back Propagation Algorithm ◽

Element Analysis ◽

Knowledge Based ◽

Numerical Studies

Parameters identification on structure subjected to moving load can be predicted by using the accurate and reliable data. The concepts of recurrent neural networks (RNNs) approach have been used in parameters (crack locations and severities) identifications in structure subjected to moving load in the present methodology. This methodology has incorporated the knowledge based Elman's recurrent neural networks (ERNNs) and Jordan's recurrent neural networks (JRNNs) jointly for the identification of parameters. This approach has been addressed as the inverse problem for predicting the locations and quantification of cracks in the structure in a supervised manner. The Levenberg-Marquardt's back propagation algorithm is implemented to train the proposed networks. To check the robustness of the present method, Numerical studies followed by Finite Element Analysis (FEA) and experimental verifications (Forward problems) are presented as a case study by considering a multi-cracked simply supported structure under a moving mass. The estimated crack locations and severities obtained from the proposed RNNs model converge well with those of FEA and experiments. From the demonstration of the case study, it concludes that the proposed analogy can identify and quantify the crack locations and severities effectively.

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