Hybrid neural network
About: Hybrid neural network is a(n) research topic. Over the lifetime, 1305 publication(s) have been published within this topic receiving 18223 citation(s).
01 Oct 1992-Aiche Journal
Abstract: A hybrid neural network-first principles modeling scheme is developed and used to model a fedbatch bioreactor. The hybrid model combines a partial first principles model, which incorporates the available prior knowledge about the process being modeled, with a neural network which serves as an estimator of unmeasuredprocess parameters that are difficult to model from first principles. This hybrid model has better properties than standard “black-box” neural network models in that it is able to interpolate and extrapolate much more accurately, is easier to analyze and interpret, and requires significantly fewer training examples. Two alternative state and parameter estimation strategies, extended Kalman filtering and NLP optimization, are also considered. When no a priori known model of the unobserved process parameters is available, the hybrid network model gives better estimates of the parameters, when compared to these methods. By providing a model of these unmeasured parameters, the hybrid network can also make predictions and hence can be used for process optimization. These results apply both when full and partial state measurements are available, but in the latter case a state reconstruction method must be used for the first principles component of the hybrid model.
01 Nov 2001-IEEE Transactions on Biomedical Engineering
TL;DR: The results of experiments of recognition of different types of beats on the basis of the ECG waveforms have confirmed good efficiency of the proposed solution and show that the method may find practical application in the recognition and classification of different type heart beats.
Abstract: Presents the application of the fuzzy neural network for electrocardiographic (ECG) beat recognition and classification. The new classification algorithm of the ECG beats, applying the fuzzy hybrid neural network and the features drawn from the higher order statistics has been proposed in the paper. The cumulants of the second, third, and fourth orders have been used for the feature selection. The hybrid fuzzy neural network applied in the solution consists of the fuzzy self-organizing subnetwork connected in cascade with the multilayer perceptron, working as the final classifier. The c-means and Gustafson-Kessel algorithms for the self-organization of the neural network have been applied. The results of experiments of recognition of different types of beats on the basis of the ECG waveforms have confirmed good efficiency of the proposed solution. The investigations show that the method may find practical application in the recognition and classification of different type heart beats.
TL;DR: A hybrid ARIMA and neural network model is proposed that is capable of exploiting the strengths of traditional time series approaches and artificial neural networks to provide a robust modeling framework capable of capturing the nonlinear nature of the complex time series and thus producing more accurate predictions.
Abstract: Accurate predictions of time series data have motivated the researchers to develop innovative models for water resources management. Time series data often contain both linear and nonlinear patterns. Therefore, neither ARIMA nor neural networks can be adequate in modeling and predicting time series data. The ARIMA model cannot deal with nonlinear relationships while the neural network model alone is not able to handle both linear and nonlinear patterns equally well. In the present study, a hybrid ARIMA and neural network model is proposed that is capable of exploiting the strengths of traditional time series approaches and artificial neural networks. The proposed approach consists of an ARIMA methodology and feed-forward, backpropagation network structure with an optimized conjugated training algorithm. The hybrid approach for time series prediction is tested using 108-month observations of water quality data, including water temperature, boron and dissolved oxygen, during 1996-2004 at Buyuk Menderes river, Turkey. Specifically, the results from the hybrid model provide a robust modeling framework capable of capturing the nonlinear nature of the complex time series and thus producing more accurate predictions. The correlation coefficients between the hybrid model predicted values and observed data for boron, dissolved oxygen and water temperature are 0.902, 0.893, and 0.909, respectively, which are satisfactory in common model applications. Predicted water quality data from the hybrid model are compared with those from the ARIMA methodology and neural network architecture using the accuracy measures. Owing to its ability in recognizing time series patterns and nonlinear characteristics, the hybrid model provides much better accuracy over the ARIMA and neural network models for water quality predictions.
01 Mar 2007-
TL;DR: The results obtained in this study suggest that the approach of combining the strengths of the conventional and ANN techniques provides a robust modelling framework capable of capturing the non-linear nature of the complex time series and thus producing more accurate forecasts.
Abstract: The need for increased accuracies in time series forecasting has motivated the researchers to develop innovative models. In this paper, a new hybrid time series neural network model is proposed that is capable of exploiting the strengths of traditional time series approaches and artificial neural networks (ANNs). The proposed approach consists of an overall modelling framework, which is a combination of the conventional and ANN techniques. The steps involved in the time series analysis, e.g. de-trending and de-seasonalisation, can be carried out before gradually presenting the modified time series data to the ANN. The proposed hybrid approach for time series forecasting is tested using the monthly streamflow data at Colorado River at Lees Ferry, USA. Specifically, results from four time series models of auto-regressive (AR) type and four ANN models are presented. The results obtained in this study suggest that the approach of combining the strengths of the conventional and ANN techniques provides a robust modelling framework capable of capturing the non-linear nature of the complex time series and thus producing more accurate forecasts. Although the proposed hybrid neural network models are applied in hydrology in this study, they have tremendous scope for application in a wide range of areas for achieving increased accuracies in time series forecasting.
01 Jul 2008-Expert Systems With Applications
TL;DR: A new method for classification of data of a medical database is presented and one of the best results compared with results obtained from related previous studies and reported in the UCI web sites is observed.
Abstract: Data can be classified according to their properties. Classification is implemented by developing a model with existing records by using sample data. One of the aims of classification is to increase the reliability of the results obtained from the data. Fuzzy and crisp values are used together in medical data. Regarding to this, a new method is presented for classification of data of a medical database in this study. Also a hybrid neural network that includes artificial neural network (ANN) and fuzzy neural network (FNN) was developed. Two real-time problem data were investigated for determining the applicability of the proposed method. The data were obtained from the University of California at Irvine (UCI) machine learning repository. The datasets are Pima Indians diabetes and Cleveland heart disease. In order to evaluate the performance of the proposed method accuracy, sensitivity and specificity performance measures that are used commonly in medical classification studies were used. The classification accuracies of these datasets were obtained by k-fold cross-validation. The proposed method achieved accuracy values 84.24% and 86.8% for Pima Indians diabetes dataset and Cleveland heart disease dataset, respectively. It has been observed that these results are one of the best results compared with results obtained from related previous studies and reported in the UCI web sites.