scholarly journals GeneSys: Enabling Continuous Learning through Neural Network Evolution in Hardware

2018 ◽  
Author(s):  
Ananda Samajdar ◽  
Parth Mannan ◽  
Kartikay Garg ◽  
Tushar Krishna
Keyword(s):  
Neural Network ◽  
Network Evolution ◽  
Continuous Learning

Load forecasting for smart grid based on continuous-learning neural network

2021 ◽  
Vol 201 ◽  
pp. 107545
Author(s):  
Marcela A. da Silva ◽  
Thays Abreu ◽  
Carlos Roberto Santos-Júnior ◽  
Carlos R. Minussi
Keyword(s):  
Neural Network ◽  
Smart Grid ◽  
Load Forecasting ◽  
Continuous Learning ◽  
Grid Based

Continuous Learning Framework for Freeway Incident Detection

1998 ◽  
Vol 1644 (1) ◽  
pp. 124-131 ◽  
Author(s):  
Srinivas Peeta ◽  
Debjit Das
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Superior Performance ◽  
Incident Detection ◽  
Neural Net ◽  
Continuous Learning ◽  
Back Propagation Algorithm ◽  
Learning Capability ◽  
Detection Algorithms ◽  
On Line

Existing freeway incident detection algorithms predominantly require extensive off-line training and calibration precluding transferability to new sites. Also, they are insensitive to demand and supply changes in the current site without recalibration. We propose two neural network-based approaches that incorporate an on-line learning capability, thereby ensuring transferability, and adaptability to changes at the current site. The least-squares technique and the error back propagation algorithm are used to develop on-line neural network-trained versions of the popular California algorithm and the more recent McMaster algorithm. Simulated data from the integrated traffic simulation model is used to analyze performance of the neural network-based versions of the California and McMaster algorithms over a broad spectrum of operational scenarios. The results illustrate the superior performance of the neural net implementations in terms of detection rate, false alarm rate, and time to detection. Of implications to current practice, they suggest that just introducing a continuous learning capability to commonly used detection algorithms in practice such as the California algorithm enhances their performance with time in service, allows transferability, and ensures adaptability to changes at the current site. An added advantage of this strategy is that existing traffic measures used (such as volume, occupancy, and so forth.) in those algorithms are sufficient, circumventing the need for new traffic measures, new threshold parameters, and variables that require subjective decisions.

A Continuous Learning Approach for Real-Time Network Intrusion Detection

2021 ◽  
Author(s):  
Marcello Rinaldo Martina ◽  
Gian Luca Foresti
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Detection System ◽  
Network Intrusion Detection ◽  
Continuous Learning ◽  
Learning Approaches ◽  
Network Connection ◽  
Network Intrusion ◽  
Learning Capabilities ◽  
High Level

Network intrusion detection is becoming a challenging task with cyberattacks that are becoming more and more sophisticated. Failing the prevention or detection of such intrusions might have serious consequences. Machine learning approaches try to recognize network connection patterns to classify unseen and known intrusions but also require periodic re-training to keep the performances at a high level. In this paper, a novel continuous learning intrusion detection system, called Soft-Forgetting Self-Organizing Incremental Neural Network (SF-SOINN), is introduced. SF-SOINN, besides providing continuous learning capabilities, is able to perform fast classification, is robust to noise, and it obtains good performances with respect to the existing approaches. The main characteristic of SF-SOINN is the ability to remove nodes from the neural network based on their utility estimate. SF-SOINN has been validated on the well-known NSL-KDD and CIC-IDS-2017 intrusion detection datasets as well as on some artificial data to show the classification capability on more general tasks.

Enhanced Data Stream Classification by Optimized Weight Updated Meta-learning: Continuous learning-based on Concept-Drift

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Maisnam Niranjan Singh ◽  
Samitha Khaiyum
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Concept Drift ◽  
Learning Model ◽  
Streaming Data ◽  
Spotted Hyena ◽  
Continuous Learning ◽  
Content Type ◽  
Data Stream Classification ◽  
Meta Learning

Purpose The aim of continuous learning is to obtain and fine-tune information gradually without removing the already existing information. Many conventional approaches in streaming data classification assume that all arrived new data is completely labeled. To regularize Neural Networks (NNs) by merging side information like user-provided labels or pair-wise constraints, incremental semi-supervised learning models need to be introduced. However, they are hard to implement, specifically in non-stationary environments because of the efficiency and sensitivity of such algorithms to parameters. The periodic update and maintenance of the decision method is the significant challenge in incremental algorithms whenever the new data arrives. Design/methodology/approach Hence, this paper plans to develop the meta-learning model for handling continuous or streaming data. Initially, the data pertain to continuous behavior is gathered from diverse benchmark source. Further, the classification of the data is performed by the Recurrent Neural Network (RNN), in which testing weight is adjusted or optimized by the new meta-heuristic algorithm. Here, the weight is updated for reducing the error difference between the target and the measured data when new data is given for testing. The optimized weight updated testing is performed by evaluating the concept-drift and classification accuracy. The new continuous learning by RNN is accomplished by the improved Opposition-based Novel Updating Spotted Hyena Optimization (ONU-SHO). Finally, the experiments with different datasets show that the proposed learning is improved over the conventional models. Findings From the analysis, the accuracy of the ONU-SHO based RNN (ONU-SHO-RNN) was 10.1% advanced than Decision Tree (DT), 7.6% advanced than Naive Bayes (NB), 7.4% advanced than k-nearest neighbors (KNN), 2.5% advanced than Support Vector Machine (SVM) 9.3% advanced than NN, and 10.6% advanced than RNN. Hence, it is confirmed that the ONU-SHO algorithm is performing well for acquiring the best data stream classification. Originality/value This paper introduces a novel meta-learning model using Opposition-based Novel Updating Spotted Hyena Optimization (ONU-SHO)-based Recurrent Neural Network (RNN) for handling continuous or streaming data. This is the first work utilizes a novel meta-learning model using Opposition-based Novel Updating Spotted Hyena Optimization (ONU-SHO)-based Recurrent Neural Network (RNN) for handling continuous or streaming data.

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