Hybrid neural network

About: Hybrid neural network is a research topic. Over the lifetime, 1305 publications have been published within this topic receiving 18223 citations.

Real-time pattern recognition in statistical process control: A hybrid neural network/decision tree-based approach:

Abstract: Pattern recognition is an important issue in statistical process control because unnatural patterns displayed by control charts can be associated with specific causes that adversely impact on the manufacturing process. A common problem of existing approaches to control chart pattern (CCP) recognition is false classification between different types of CCP that share similar features in a real-time process-monitoring scenario, in which only limited pattern points are available for recognition. This study proposes a hybrid learning-based system that integrates neural networks and decision tree learning to overcome the classification problem in a real-time CCP recognition scheme. This hybrid system consists of three sequential modules, namely feature extraction, coarse classification, and fine classification. The coarse-classification model employs a four-layer back propagation network to detect and classify unnatural CCPs. The fine-classification module contains four decision trees used in a simple h...

Surrogate Modeling of Electrical Machine Torque Using Artificial Neural Networks

Abstract: Machine learning and artificial neural networks have shown to be applicable in modeling and simulation of complex physical phenomena as well as creating surrogate models trained with physics-based simulation data for numerous applications that require good computational performance. In this article, we review widely the surrogate modeling concept and its applications in the electrical machine context. We present comprehensively a workflow for developing data-driven surrogate models including data generation with physics-based simulation and design of experiments, preprocessing of training data, and training and testing of the surrogates. We compare neural networks and gradient boosting decision trees in modeling and simulation of torque behavior of a permanent magnet synchronous machine together with selected design of experiments approaches with respect to surrogate accuracy and computational efficiency. In addition, an approach to utilizing domain knowledge to create a hybrid surrogate model in order to improve the surrogate accuracy is shown. The accuracy of the selected hybrid neural network was better than with the gradient boosting approach and was close to the finite element simulation, whereas its run-time efficiency was significantly better compared to the finite element simulation with a speed-up factor of over 2,000. In addition, combining the sampling methods provided better results than the selected methods alone.

MobileRF: a robust device-free tracking system based on a hybrid neural network HMM classifier

Abstract: We present a device-free indoor tracking system that uses received signal strength (RSS) from radio frequency (RF) transceivers to estimate the location of a person. While many RSS-based tracking systems use a body-worn device or tag, this approach requires no such tag. The approach is based on the key principle that RF signals between wall-mounted transceivers reflect and absorb differently depending on a person's movement within their home. A hierarchical neural network hidden Markov model (NN-HMM) classifier estimates both movement patterns and stand vs. walk conditions to perform tracking accurately. The algorithm and features used are specifically robust to changes in RSS mean shifts in the environment over time allowing for greater than 90% region level classification accuracy over an extended testing period. In addition to tracking, the system also estimates the number of people in different regions. It is currently being developed to support independent living and long-term monitoring of seniors.

Toward Smart Lockdown: A Novel Approach for COVID-19 Hotspots Prediction Using a Deep Hybrid Neural Network

Abstract: COVID-19 caused the largest economic recession in the history by placing more than one third of world’s population in lockdown. The prolonged restrictions on economic and business activities caused huge economic turmoil that significantly affected the financial markets. To ease the growing pressure on the economy, scientists proposed intermittent lockdowns commonly known as “smart lockdowns”. Under smart lockdown, areas that contain infected clusters of population, namely hotspots, are placed on lockdown, while economic activities are allowed to operate in un-infected areas. In this study, we proposed a novel deep learning prediction framework for the accurate prediction of hotpots. We exploit the benefits of two deep learning models, i.e., Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) and propose a hybrid framework that has the ability to extract multi time-scale features from convolutional layers of CNN. The multi time-scale features are then concatenated and provide as input to 2-layers LSTM model. The LSTM model identifies short, medium and long-term dependencies by learning the representation of time-series data. We perform a series of experiments and compare the proposed framework with other state-of-the-art statistical and machine learning based prediction models. From the experimental results, we demonstrate that the proposed framework beats other existing methods with a clear margin.

Categorization of Fetal Heart Rate Patterns Using Neural Networks

Abstract: Digitized data from CTG (cardiotocography) measurements (fetal heart rate and uterine contractions) have been used for categorization of typical heart rate patterns before and during delivery. Short time series of CTG data, about 7 min duration, have been used in the categorization process. In the first part of the study, selected CTG data corresponding to 10 typical cases were used for purely auto associative unsupervised training of a Self-Organizing Map Neural Network (SOM). The network may then be used for objective categorization of CTG patterns through the map coordinates produced by the network. The SOM coordinates were then compared. In the second part of the study, a hybrid neural network consisting of a SOM network and a Back-Propagation network (BP) was trained with data corresponding to a number of basic heart rate patterns as described by eight manually selected indices. Test data (different than the training data) were then used to check the performance of the network. The present study shows that the categorization process, in which neural networks were used, can be reliable and agree well with the manual categorization. Since the categorization by neural networks is very fast and does not involve human efforts, it may be useful in patient monitoring.