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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 ArticleDOI
TL;DR: This paper focuses on an efficient cost-sensitive parallel learning framework (CPLF) to enhance insurance operations with a deep learning approach that does not require preprocessing and the results of comparative experiments on real-world insurance data sets reflecting actual business cases demonstrate the effectiveness of the design.
Abstract: Recent advancements in artificial intelligence are providing the insurance industry with new opportunities to create tailored solutions and services based on newfound knowledge of consumers, and the execution of enhanced operations and business functions. However, insurance data are heterogeneous, and imbalanced class distribution with low frequency and high dimensions, which presents four major challenges to machine learning in real-world business. Traditional machine learning algorithms can typically apply to standard data sets, which are normally homogeneous and balanced. In this paper, we focus on an efficient cost-sensitive parallel learning framework (CPLF) to enhance insurance operations with a deep learning approach that does not require preprocessing. Our approach comprises a novel, unified, end-to-end cost-sensitive parallel neural network that learns real-world heterogeneous data. A specifically designed cost-sensitive matrix then automatically generates a robust model for learning minority classifications, and the parameters of both the cost-sensitive matrix and the hybrid neural network are alternately but jointly optimized during training. We also study the CPLF-based architecture for a real-world insurance intelligence operation system, and demonstrate fraud detection and policy renewal experiments on this system. The results of comparative experiments on real-world insurance data sets reflecting actual business cases demonstrate the effectiveness of our design.

15 citations

Proceedings ArticleDOI
Amir Hussain1
21 Apr 1997
TL;DR: A new two-layer linear-in-the-parameters feedforward network termed the functionally expanded neural network (FENN) is presented, together with its design strategy and learning algorithm which is essentially a hybrid neural network incorporating a variety of non-linear basis functions within its single hidden layer.
Abstract: A new two-layer linear-in-the-parameters feedforward network termed the functionally expanded neural network (FENN) is presented, together with its design strategy and learning algorithm. It is essentially a hybrid neural network incorporating a variety of non-linear basis functions within its single hidden layer which emulate other universal approximators employed in the conventional multi-layered perceptron (MLP), radial basis function (RBF) and Volterra neural networks (VNN). The FENN's output error surface is shown to be uni-modal allowing high speed single run learning. A simple strategy based on an iterative pruning retraining scheme coupled with statistical model validation tests is proposed for pruning the FENN. Both simulated chaotic (Mackey-Glass time series) and real-world noisy, highly nonstationary (sunspot) time series are used to illustrate the superior modeling and prediction performance of the FENN compared with other previously reported, more complex neural network based predictor models.

15 citations

Proceedings ArticleDOI
23 Mar 2011
TL;DR: In this paper, a hybrid neural network and fuzzy control is proposed for automatic generation control in thermal power systems, where a recurrent neural network is employed to forecast controller and system's future output, based on the current Area Control Error (ACE) and the predicted change of ACE.
Abstract: The AGC of reheat interconnected two area power systems are characterized by non-linearity and uncertainty. A hybrid neural network and fuzzy control is proposed for automatic generation control in power systems. Recurrent neural network is employed to forecast controller and system's future output, based on the current Area Control Error (ACE) and the predicted change-of-ACE. The Control Performance Standard (CPS) criterion is adapted to the fuzzy controller design, thus improves the dynamic quality of system. The system was simulated and the frequency deviations in area 1 and area 2 and tie-line power deviations for 1% step-load disturbance in area 1 were obtained. The comparison of frequency deviations and tie-line power deviations for the two area interconnected thermal power system integral controller with Redox Flow Batteries (RFB) reveals that the system with hybrid fuzzy neural controller enhances a better stability than that of system without hybrid fuzzy neural controller.

15 citations

Proceedings ArticleDOI
01 Jan 2006
TL;DR: The hybrid GR is considered an enhanced version of GRNN with compression ability while still maintains online learning properties, and simulation results show that GR has comparable prediction accuracy and has less prototype as compared to the original GRNN as well as the support vector regression.
Abstract: Short term load forecasting (STLF) is very important from the power systems grid operation point of view. STLF involves forecasting load demand in a short term time frame. The short term time frame may consist of half hourly prediction up to weekly prediction. Accurate forecasting would benefit the utility in terms of reliability and stability of the grid ensuring adequate supply is present to meet with the load demand. Apart from that it would also affect the financial performance of the utility company. An accurate forecast would result in better savings while maintaining the security of the grid. This paper outlines the STLF using a novel hybrid online learning neural network, known as the Gaussian regression (GR). This new hybrid neural network is a combination of two existing online learning neural networks which are the Gaussian adaptive resonance theory (GA) and the generalized regression neural network (GRNN). Both GA and GRNN implemented online learning, but each of them suffers from limitation. Originally GA is used for unsupervised clustering by compressing the training samples into several categories. A supervised version of GA is available, namely Gaussian ARTMAP (GAM). However, the GAM is still not capable on solving regression problem. On the other hand, GRNN is designed for solving real value estimation (regression) problem, but the learning process would involve of memorizing all training samples, hence high computational cost. The hybrid GR is considered an enhanced version of GRNN with compression ability while still maintains online learning properties. Simulation results show that GR has comparable prediction accuracy and has less prototype as compared to the original GRNN as well as the support vector regression.

14 citations

Journal ArticleDOI
TL;DR: Two neural network approaches - a moving-window and hybrid neural network - which combine neural network with polynomial regression models, were used for modeling F( t) and Qv(t) dynamic functions under constant retort temperature processing, demonstrating that both neural network models well described the F(t).
Abstract: Two neural network approaches - a moving-window and hybrid neural network - which combine neural network with polynomial regression models, were used for modeling F(t) and Qv(t) dynamic functions under constant retort temperature processing. The dynamic functions involved six variables: retort temperature (116-132C), thermal diffusivity (1.5-2.3 x 10 -7 m 2 /s ), can radius (40-61 mm), can height (40-61 mm), and quality kinetic parameters z (15-39C) and D (150-250 min). A computer simulation designed for process calculations of food thermal processing systems was used to provide the fundamental data for training and generalization of ANN models. Training data and testing data were constructed by both second order central composite design and orthogonal array, respectively. The optimal configurations of ANN models were obtained by varying the number of hidden layers, number of neurons in hidden layer and learning runs, and a combination of learning rules and transfer function. Results demonstrated that both neural network models well described the F(t) and Qv(t) dynamic functions, but moving-window network had better modeling performance than the hybrid ANN models. By comparison of the configuration parameters, moving-window ANN models required more neurons in the hidden layer and more learning runs for training than the hybrid ANN models. Deux approches par reseaux neuronaux combines avec des modeles de regression polynomiaux sont utilisees pour modeliser les fonctions dynamiques F(t) et Qv(t) dans un procede d'autoclavage a temperature constante. Une simulation par ordinateur destinee aux calculs de procedes des systemes thermiques alimentaires est utilisee pour fournir les donnees fondamentales des modeles. Le reseau par fenetre mobile montre des performances de modelisation superieures a celles du modele hybride.

14 citations


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