A Novel Approach for High-Level Power Modeling of Sequential Circuits Using Recurrent Neural Networks

2005 ◽  
Author(s):  
Wen-Tsan Hsieh ◽  
Chih-Chieh Shiue ◽  
Chien-Nan Jimmy Liu
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Sequential Circuits ◽  
Power Modeling ◽  
Novel Approach ◽  
High Level

scholarly journals Explainable Hopfield Neural Networks by Using an Automatic Video Generation System

2021 ◽  
Author(s):  
Clemente Rubio Manzano
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Software Visualization ◽  
Hopfield Neural Networks ◽  
Generation System ◽  
Novel Approach ◽  
Systems Software ◽  
Explainable Artificial Intelligence ◽  
Technical Details ◽  
Artificial Intelligence Systems

Hopfield Neural Networks (HNNs) are recurrent neural networks used to implement associative memory. Their main feature is their ability to pattern recognition, optimization, or image segmentation. However, sometimes it is not easy to provide the users with good explanations about the results obtained with them due to mainly the large number of changes in the state of neurons (and their weights) produced during a problem of machine learning. There are currently limited techniques to visualize, verbalize, or abstract HNNs. This paper outlines how we can construct automatic video generation systems to explain their execution. This work constitutes a novel approach to get explainable artificial intelligence systems in general and HNNs in particular building on the theory of data-to-text systems and software visualization approaches. We present a complete methodology to build these kinds of systems. Software architecture is also designed, implemented, and tested. Technical details about the implementation are also detailed and explained. Finally, we apply our approach for creating a complete explainer video about the execution of HNNs on a small recognition problem.

scholarly journals A Novel Approach to Condition Monitoring of the Cutting Process Using Recurrent Neural Networks

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4493
Author(s):  
Rui Silva ◽  
António Araújo
Keyword(s):  
Neural Networks ◽  
Condition Monitoring ◽  
Recurrent Neural Networks ◽  
Cutting Process ◽  
Time Dependent ◽  
Industrial Processes ◽  
Fundamental Part ◽  
Novel Approach ◽  
Noisy Signals ◽  
Proper Condition

Condition monitoring is a fundamental part of machining, as well as other manufacturing processes where, generally, there are parts that wear out and have to be replaced. Devising proper condition monitoring has been a concern of many researchers, but there is still a lack of robustness and efficiency, most often hindered by the system’s complexity or otherwise limited by the inherent noisy signals, a characteristic of industrial processes. The vast majority of condition monitoring approaches do not take into account the temporal sequence when modelling and hence lose an intrinsic part of the context of an actual time-dependent process, fundamental to processes such as cutting. The proposed system uses a multisensory approach to gather information from the cutting process, which is then modelled by a recurrent neural network, capturing the evolutive pattern of wear over time. The system was tested with realistic cutting conditions, and the results show great effectiveness and accuracy with just a few cutting tests. The use of recurrent neural networks demonstrates the potential of such an approach for other time-dependent industrial processes under noisy conditions.

scholarly journals Bispectrum and Recurrent Neural Networks: Improved Classification of Interictal and Preictal States

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Laura Gagliano ◽  
Elie Bou Assi ◽  
Dang K. Nguyen ◽  
Mohamad Sawan
Keyword(s):  
Neural Networks ◽  
Multilayer Perceptron ◽  
Recurrent Neural Networks ◽  
Short Term Memory ◽  
Focal Epilepsy ◽  
Single Layer ◽  
Epileptic Seizures ◽  
Novel Approach ◽  
Multilayer Perceptron Networks

Abstract This work proposes a novel approach for the classification of interictal and preictal brain states based on bispectrum analysis and recurrent Long Short-Term Memory (LSTM) neural networks. Two features were first extracted from bilateral intracranial electroencephalography (iEEG) recordings of dogs with naturally occurring focal epilepsy. Single-layer LSTM networks were trained to classify 5-min long feature vectors as preictal or interictal. Classification performances were compared to previous work involving multilayer perceptron networks and higher-order spectral (HOS) features on the same dataset. The proposed LSTM network proved superior to the multilayer perceptron network and achieved an average classification accuracy of 86.29% on held-out data. Results imply the possibility of forecasting epileptic seizures using recurrent neural networks, with minimal feature extraction.

scholarly journals A novel approach based on recurrent neural networks applied to nonlinear systems optimization

2007 ◽  
Vol 31 (1) ◽  
pp. 78-92 ◽  
Author(s):  
Ivan Nunes da Silva ◽  
Wagner Caradori do Amaral ◽  
Lucia Valeria de Arruda
Keyword(s):  
Neural Networks ◽  
Nonlinear Systems ◽  
Recurrent Neural Networks ◽  
Novel Approach

scholarly journals Predictive Monitoring with Logic-Calibrated Uncertainty for Cyber-Physical Systems

2021 ◽  
Vol 20 (5s) ◽  
pp. 1-25
Author(s):  
Meiyi Ma ◽  
John Stankovic ◽  
Ezio Bartocci ◽  
Lu Feng
Keyword(s):  
Neural Networks ◽  
Real World ◽  
Recurrent Neural Networks ◽  
Cyber Physical Systems ◽  
Uncertainty Estimation ◽  
Physical Systems ◽  
Confidence Levels ◽  
Novel Approach ◽  
Infinite Set

Predictive monitoring—making predictions about future states and monitoring if the predicted states satisfy requirements—offers a promising paradigm in supporting the decision making of Cyber-Physical Systems (CPS). Existing works of predictive monitoring mostly focus on monitoring individual predictions rather than sequential predictions. We develop a novel approach for monitoring sequential predictions generated from Bayesian Recurrent Neural Networks (RNNs) that can capture the inherent uncertainty in CPS, drawing on insights from our study of real-world CPS datasets. We propose a new logic named Signal Temporal Logic with Uncertainty (STL-U) to monitor a flowpipe containing an infinite set of uncertain sequences predicted by Bayesian RNNs. We define STL-U strong and weak satisfaction semantics based on whether all or some sequences contained in a flowpipe satisfy the requirement. We also develop methods to compute the range of confidence levels under which a flowpipe is guaranteed to strongly (weakly) satisfy an STL-U formula. Furthermore, we develop novel criteria that leverage STL-U monitoring results to calibrate the uncertainty estimation in Bayesian RNNs. Finally, we evaluate the proposed approach via experiments with real-world CPS datasets and a simulated smart city case study, which show very encouraging results of STL-U based predictive monitoring approach outperforming baselines.

NONLINEAR SYSTEM IDENTIFICATION BASED ON INTERNAL RECURRENT NEURAL NETWORKS

2009 ◽  
Vol 19 (02) ◽  
pp. 115-125 ◽  
Author(s):  
GHEORGHE PUSCASU ◽  
BOGDAN CODRES ◽  
ALEXANDRU STANCU ◽  
GABRIEL MURARIU
Keyword(s):  
Neural Networks ◽  
System Identification ◽  
Nonlinear System ◽  
Recurrent Neural Networks ◽  
Internal State ◽  
Nonlinear System Identification ◽  
Backpropagation Algorithm ◽  
Novel Approach ◽  
System States

A novel approach for nonlinear complex system identification based on internal recurrent neural networks (IRNN) is proposed in this paper. The computational complexity of neural identification can be greatly reduced if the whole system is decomposed into several subsystems. This approach employs internal state estimation when no measurements coming from the sensors are available for the system states. A modified backpropagation algorithm is introduced in order to train the IRNN for nonlinear system identification. The performance of the proposed design approach is proven on a car simulator case study.

Convolutional Recurrent Neural Networks for Text Classification

2021 ◽  
Vol 32 (4) ◽  
pp. 65-82
Author(s):  
Shengfei Lyu ◽  
Jiaqi Liu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Recurrent Neural Network ◽  
Text Classification ◽  
Recurrent Neural Networks ◽  
Superior Performance ◽  
Novel Approach ◽  
Hidden States ◽  
Traditional Approaches

Recurrent neural network (RNN) and convolutional neural network (CNN) are two prevailing architectures used in text classification. Traditional approaches combine the strengths of these two networks by straightly streamlining them or linking features extracted from them. In this article, a novel approach is proposed to maintain the strengths of RNN and CNN to a great extent. In the proposed approach, a bi-directional RNN encodes each word into forward and backward hidden states. Then, a neural tensor layer is used to fuse bi-directional hidden states to get word representations. Meanwhile, a convolutional neural network is utilized to learn the importance of each word for text classification. Empirical experiments are conducted on several datasets for text classification. The superior performance of the proposed approach confirms its effectiveness.

scholarly journals Explainable Hopfield Neural Networks Using an Automatic Video-Generation System

2021 ◽  
Vol 11 (13) ◽  
pp. 5771
Author(s):  
Clemente Rubio-Manzano ◽  
Alejandra Segura-Navarrete ◽  
Claudia Martinez-Araneda ◽  
Christian Vidal-Castro
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Software Visualization ◽  
Hopfield Neural Networks ◽  
Generation System ◽  
Novel Approach ◽  
Systems Software ◽  
Explainable Artificial Intelligence ◽  
Technical Details ◽  
Artificial Intelligence Systems

Hopfield Neural Networks (HNNs) are recurrent neural networks used to implement associative memory. They can be applied to pattern recognition, optimization, or image segmentation. However, sometimes it is not easy to provide the users with good explanations about the results obtained with them due to mainly the large number of changes in the state of neurons (and their weights) produced during a problem of machine learning. There are currently limited techniques to visualize, verbalize, or abstract HNNs. This paper outlines how we can construct automatic video-generation systems to explain its execution. This work constitutes a novel approach to obtain explainable artificial intelligence systems in general and HNNs in particular building on the theory of data-to-text systems and software visualization approaches. We present a complete methodology to build these kinds of systems. Software architecture is also designed, implemented, and tested. Technical details about the implementation are also detailed and explained. We apply our approach to creating a complete explainer video about the execution of HNNs on a small recognition problem. Finally, several aspects of the videos generated are evaluated (quality, content, motivation and design/presentation).

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