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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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Proceedings ArticleDOI
22 Mar 1999
TL;DR: A new method for extracting features from photographic images using multiple self-organizing feature maps in a hierarchical manner and using a certain degree of supervision, an acceptable classification is obtained when applied to test images.
Abstract: A new method for extracting features from photographic images has been developed. The input image is through a pulse coupled neural network transformed to a set of signatures, well suited for classification by unsupervised neural networks. A strategy using multiple self-organizing feature maps in a hierarchical manner is developed. With this approach, using a certain degree of supervision, an acceptable classification is obtained when applied to test images. The method is applied to license plate recognition.

13 citations

Proceedings ArticleDOI
27 Jun 2009
TL;DR: Results show that the proposed conceptual cost estimates can be deployed as accurate cost estimators during early stages of construction projects, and considering linear and non-linear neuron layer connectors in EFHNN surpasses models with singular linear deployment of NN.
Abstract: Conceptual cost estimates are important to project feasibility studies, even the final project success. The estimates provide significant information for project evaluations, engineering designs, cost budgeting and cost management. This study proposes an artificial intelligence approach, the evolutionary fuzzy hybrid neural network (EFHNN), to improve precision of conceptual cost estimates. The approach incorporates neural networks (NN) and high order neural networks (HONN) into a hybrid neural network (HNN). The HNN operates with the alternative of linear and non-linear neuron layer connectors. Besides, fuzzy logic (FL) is employed for handling uncertainties, the approach therefore evolve into a fuzzy hybrid neural network (FHNN). For FHNN optimization, the genetic algorithm is used for both FL and HNN, consequently the approach is named as EFHNN. In practical case studies, two estimates including overall and category cost estimates are provided and compared. Results show that the proposed conceptual cost estimates can be deployed as accurate cost estimators during early stages of construction projects. Moreover, considering linear and non-linear neuron layer connectors in EFHNN surpasses models with singular linear deployment of NN.

13 citations

Journal ArticleDOI
TL;DR: A hybrid neural network prediction model based on recurrent neural network (RNN) and fully connected neuralnetwork (FC) is proposed to predict lane-changing behavior accurately and improve the prospective time of prediction.
Abstract: A correct lane-changing plays a crucial role in traffic safety. Predicting the lane-changing behavior of a driver can improve the driving safety significantly. In this paper, a hybrid neural network prediction model based on recurrent neural network (RNN) and fully connected neural network (FC) is proposed to predict lane-changing behavior accurately and improve the prospective time of prediction. The dynamic time window is proposed to extract the lane-changing features which include driver physiological data, vehicle kinematics data, and driver kinematics data. The effectiveness of the proposed model is validated through the experiments in real traffic scenarios. Besides, the proposed model is compared with five prediction models, and the results show that the proposed prediction model can effectively predict the lane-changing behavior more accurate and earlier than the other models. The proposed model achieves the prediction accuracy of 93.5% and improves the prospective time of prediction by about 2.1 s on average.

13 citations

Proceedings ArticleDOI
12 Oct 1997
TL;DR: The paper presents a hybrid neural network system for automatic target recognition, or ATR, that uses a hybrid of a biological inspired neural net called the Pulse Coupled Neural Net, PCNN, and traditional feedforward neural nets.
Abstract: The paper presents a hybrid neural network system for automatic target recognition, or ATR. The ATR system uses a hybrid of a biological inspired neural net called the Pulse Coupled Neural Net, PCNN, and traditional feedforward neural nets. The PCNN is an iterative neural network in which, for example, a grey scale input image results in a 1D time signal invariant to rotation, scale and translation alternations. The PCNN can also extract edges, perform object segmentation and extract texture information. The PCNN pre-processor generates a 1D time signal that is input to a feedforward pattern recognition net.

13 citations

Journal ArticleDOI
TL;DR: The paper presents the application of self‐organizing neural network for the location of the fault in the transmission line and estimation of the parameter of the faulty element and the results of computer experiments are given.
Abstract: The paper presents the application of self‐organizing neural network for the location of the fault in the transmission line and estimation of the parameter of the faulty element. The location of fault is done on the basis of the measurement of some node voltages of the line and appropriate preprocessing it to enhance the differences between different faults. The hybrid neural network is used to solve the problem. The self‐organizing layer of this network is used as the classifier. The output postprocessing MLP structure realizes the association of the place of the fault and its parameter with the measured set of node voltages. The results of computer experiments are given in the paper and discussed.

13 citations


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