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 Bankruptcy Prediction: An Integration of Financial Ratios, Intellectual Capital Ratios, MDA, and Neural Network Learning

Abstract: One purpose of this paper is to propose the hybrid neural network models for bankruptcy prediction The proposed hybrid neural network models are, respectively, a MDA model integrated with financial ratios, a MDA model integrated with financial ratios and intellectual capital ratios, a MDA-assisted neural network model integrated with financial ratios, and a MDA-assisted neural network model integrated with financial ratios and intellectual capital ratios The performance of the hybrid neural network model is compared with MDA model integrated with financial ratios as a benchmark Empirical results using Taiwan bankruptcy data show that hybrid neural network models are very promising ones in terms of accuracy and adaptability

Hybrid neural network topology (HNNT) for line outage contingency ranking

Abstract: The line outage contingency was identified as one of the contributors to voltage instability problem This event has led to significant financial losses in power system resulted from the failure in power operation and energy delivery This paper presents a hybrid neural network topology (HNNT) for line outage contingency ranking HNNT is a combination of artificial neural network (ANN) with a loading classifier and fundamental expert system modules The post-outage severity was predicted by an ANN module trained using the Levenberg-Marquardt modified backpropagation A line-based voltage stability index termed as fast voltage stability index (FVSI) was utilized as the indicator Loading classifier distributed the post-outage severity into their respective loading condition The contingency severities were consequently ranked into four categories using a rule-based module (RBM) that acts as the fundamental expert system Validation was performed on the IEEE Reliability Test System (RTS) and results indicated that the proposed HNNT can be applied practically

Improved detection of biological substances using a hybrid neural network and infrared absorption spectroscopy

Abstract: Considers basic problems that are associated with the detection of any biological substance in complex aqueous solutions using infrared absorption spectroscopy: (1) the intrinsic high background absorption of water, (2) the large number of overlapping IR absorption peaks of other molecules, and (3) the degradation of the signal of interest due to noise (usually caused by the sensing instrument itself and interference from other molecules) As a means to overcome these problems, a robust artificial neural network (ANN) detection method has been developed, which processes infrared absorption spectral data and provides a concentration decision for the dissolved substance of interest The ANN is a hybrid structure consisting of a feedforward perceptron and a counterpropagation architecture >

The Estimation of Stoneley Wave Velocity from Conventional Well Log Data: Using an Integration of Artificial Neural Networks

Abstract: Stoneley wave velocity (Vst) is capable of providing accurate data for reservoir characterization objectives, such as permeability estimation, fracture evaluation, formation anisotropy identification, etc. At the first stage of this study, different types of artificial neural networks, including generalized regression neural network, radial basis neural network, and feed-forward backpropagation neural network were utilized to predict Vst from conventional well log data. Consequently, a generalized regression neural network was employed to combine results of mentioned artificial neural networks for overall estimation of Vst. This novel hybrid method can enhance the accuracy of final prediction through reaping the benefits of individual artificial neural networks. The proposed methodology, hybrid neural network, was applied in Asmari formation, which is the major carbonate reservoir rock of Iranian southern oil field. A group of 1,640 data points was used to establish the intelligent model, and a group of 8...

Analysis of Hybrid Neural Network for Improved Performance

Abstract: this paper we take a close look at the Hybrid Neural Network Model. Hybrid model is attained by combining two Artificial Neural Networks (ANNs). In which the first model is used to perform the feature extraction task and the second one performs prediction task. This paper explores the classifying ability of the proposed hybrid model. We analyze the performance of the model, which is a compound characteristic, of which the prediction accuracy is the most important component. If the prediction accuracy of the model can be increased it will result into enhanced performance of the model. The model that has been built is under the umbrella of pattern recognition and incorporates some of the data mining techniques. Kernel Principal Component Analysis (KPCA) has been implemented in the pre-processing stage for easier subsequent analysis. By the end of the paper, the key factors that enhance the accuracy of the model have been identified and their role explained. It also has been shown that single ANN model's performance deteriorates on an unseen problem much more as compared to the hybrid model. The aim is to provide a model having better performance and accuracy. The paper focuses on the real world applications of the model.