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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.


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Journal Article
TL;DR: In this article, the authors proposed a hybrid neural network consisting of ART-2 for clustering and perceptron for preprocessing of images, which can be used in mobile robots for recognition of new objects or scenes in sight the robot during his movement.
Abstract: The model of the hybrid neural network is considered. This model consists of model ART-2 for clustering and perceptron for preprocessing of images. The perceptron provides invariant recognition of objects. This model can be used in mobile robots for recognition of new objects or scenes in sight the robot during his movement.

9 citations

Book ChapterDOI
03 Jul 2020
TL;DR: A hybrid neural network named RBERT-C for text classification to capture user intent, using the Chinese pre-trained RoBERTa to initialize representation layer parameters and outperforming a number of baseline methods.
Abstract: User intent classification plays a critical role in identifying the interests of users in question-answering and spoken dialog systems. The question texts of these systems are usually short and their conveyed semantic information are frequently insufficient. Therefore, the accuracy of user intent classification related to user satisfaction may be affected. To address the problem, this paper proposes a hybrid neural network named RBERT-C for text classification to capture user intent. The network uses the Chinese pre-trained RoBERTa to initialize representation layer parameters. Then, it obtains question representations through a bidirectional transformer structure and extracts essential features using a Convolutional Neural Network after question representation modeling. The evaluation is based on the publicly available dataset ECDT containing 3736 labeled sentences. Experimental result indicates that our model RBERT-C achieves a F1 score of 0.96 and an accuracy of 0.96, outperforming a number of baseline methods.

9 citations

Proceedings ArticleDOI
Xinying Miao1, Changhui Deng1, Xiangjun Li1, Yanping Gao1, Donggang He1 
23 Oct 2010
TL;DR: GA-LM, a neural network model combining Levenberg–Marquardt(LM) algorithm and Genetic Algorithm was developed for predicting DO in an aquaculture pond at Dalian, China, and can offer stronger and better performance when used as a quick interpolation and extrapolation tool.
Abstract: The prediction for dissolved oxygen (DO) in aquaculture ponds is a problem of multi-variables, nonlinearity and long-time lag. Neural networks (NNs) have become one of ideal tools in modeling nonlinear relationship between inputs and outputs. In this work, GA-LM, a neural network model combining Levenberg–Marquardt(LM) algorithm and Genetic Algorithm (GA) was developed for predicting DO in an aquaculture pond at Dalian, China. LM was used to train NNs, showing faster convergence rate. The network architecture was optimized by GA. The performance of GA-LM has been compared with that of conventional Back-Propagation (BP) algorithm and Levenberg–Marquardt(LM) algorithm. The comparison indicates that the predicted DO values using GA-LM model are in good agreement with the measured data. It is demonstrated here that the model is capable of predicting DO accurately, and can offer stronger and better performance than conventional neural networks when used as a quick interpolation and extrapolation tool.

9 citations

Proceedings ArticleDOI
08 Jul 1991
TL;DR: An ongoing comprehensive program to develop and assess neural networks for Naval FCS applications was discussed, and some initial favorable results were obtained.
Abstract: Summary form only given. Neural networks have the potential to overcome some of the most difficult problems that occur in the design and implementation of modern flight control systems (FCS). An ongoing comprehensive program to develop and assess this technology for Naval FCS applications was discussed. Currently, this program is focused on the development of a neural network FCS design tool, a neural network flight control computer emulator, a hybrid neural network/fuzzy logic automatic aircraft carrier landing system, and a neural network flight control configuration management system. For the neural flight control design tool, some initial favorable results were obtained. >

9 citations

Journal ArticleDOI
TL;DR: The results show that the model developed using the proposed hybrid algorithm based on GA with gradient descent algorithm give better results as compared to the work presented by other authors in literature, and parallel computing is beneficial in reducing the model training time.
Abstract: The various software metrics proposed in the literature can be used to evaluate the quality of software systems written in object-oriented manner. These metrics are broadly categorized into two subcategories i.e., system level software metrics and class level software metrics. In this work, ten different types of class level metrics are considered as an input to develop one model for predicting software maintainability of object-oriented software system. These models are developed using three types of neural networks, i.e., artificial neural network, radial basis function network, and functional link artificial neural network. In this study, a hybrid algorithm based on genetic algorithm (GA) with gradient descent algorithm has been proposed to find optimal weights of these neural networks. Since accuracy of the prediction model is highly dependent on the class level metrics, they are considered as input of the models. So, five different feature selection techniques are used in this study to identify the best set of features with an objective to improve the accuracy of software maintainability prediction model. The effectiveness of these models are evaluated using four evaluation metrics, i.e., MAE, MMRE, RMSE, and SEM. In this work, parallel computing concept has been also considered with an objective to reduce the model training time. The results show that the model developed using the proposed hybrid algorithm based on GA with gradient descent algorithm give better results as compared to the work presented by other authors in literature. The results also show that feature selection techniques obtain better results for predicting maintainability as compared to all metrics. The experimental results show that parallel computing is beneficial in reducing the model training time.

9 citations


Network Information
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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20233
20228
2021128
2020119
2019104
201863