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.

Hybrid Neural Network Models for Hydrologic Time Series Forecasting Based on Genetic Algorithm

Abstract: Hydrologic time series forecasting is very an important area in water resource. Based on the multi-time scale and the nonlinear characteristics of the rainfall-runoff time series, a new hybrid neural network (NN) has been suggested by Genetic Algorithm (GA) selection the lag period of time series for NN input variables, optimization neural network architecture and connection weights. The evolved neural network architecture and connection weights are then input into a new neural network. The new neural network is trained using back -- propagation (BP) algorithm for hydrologic time series forecasting. The ensemble strategy is implemented using the quadratic programming. The present model absorbs some merits of GA and artificial neural network. Case studies, the short and long term prediction of hydrological time series, have been researched. The comparison results revealed that the suggested model could increase the forecasted accuracy and prolong the length time of prediction.

Neural/fuzzy systems for incipient fault detection in induction motors

Abstract: Industrial motors are subject to incipient faults which, if undetected, can lead to motor failure. The necessity of incipient fault detection can be justified by safety and economical reasons. The technology of artificial neural networks has been successfully used to solve the motor incipient fault detection problem. The artificial neural network, however, does not provide any heuristic knowledge of the fault detection procedure. This paper will introduce a hybrid neural network/fuzzy logic system that not only provides better performance on detecting motor faults, but also allows heuristic interpretation of the network fault detection process. The system will be applied to bearing faults in single phase induction motors. The paper will discuss how to extract heuristic information from the system to gain further insight into the motor fault detection procedure. >

Impact of hybrid neural network on the early diagnosis of diabetic retinopathy disease from video-oculography signals

Abstract: In this study, we introduce two hybrid artificial neural network models with particle swarm optimization algorithm to diagnose diabetic retinopathy based on the Video-Oculography signals. The hybrid models use Discrete Wavelet Transform and Hilbert-Huang Transform separately to extract features from the signals. The classification performance of both models is analyzed comparatively. We show that the model based on Hilbert–Huang Transform exhibits better classification performance than the model based on the Discrete Wavelet Transform.

Application of InP Neural Network to ECG BeatClassification

Abstract: This paper presents an application of a hybrid neural network structure to the classification of the electrocardiogram (ECG) beats. Three different feature extraction methods are comparatively examined: discrete cosine transform, wavelet transform and a direct method. Classification performances, training times and the numbers of nodes of Kohonen network, Restricted Coulomb Energy (RCE) network and the hybrid neural network are presented. To increase the classification performance and to decrease the number of nodes, the hybrid neural network is trained by Genetic Algorithms (GAs). Ten types of ECG beats obtained from the MIT-BIH database and from a real-time ECG measurement system are classified with a success of 98% by using the hybrid neural network structure and discrete cosine transform together.