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.

Experimental and Numerical Investigation of an Innovative Method for Strengthening Cold-Formed Steel Profiles in Bending throughout Finite Element Modeling and Application of Neural Network Based on Feature Selection Method

Abstract: This study evaluates an innovative reinforcement method for cold-formed steel (CFS) upright sections through finite element assessment as well as prediction of the normalized ultimate load and deflection of the profiles by artificial intelligence (AI) and machine learning (ML) techniques. Following the previous experimental studies, several CFS upright profiles with different lengths, thicknesses and reinforcement spacings are modeled and analyzed under flexural loading. The finite element method (FEM) is employed to evaluate the proposed reinforcement method in different upright sections and to provide a valid database for the analytical study. To detect the most influential factor on flexural strength, the “feature selection” method is performed on the FEM results. Then, by using the feature selection method, a hybrid neural network (a combination of multi-layer perceptron algorithm and particle swarm optimization method) is developed for the prediction of normalized ultimate load. The correlation coefficient (R), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), mean absolute error (MAE) and Wilmot’s index of agreement (WI) are used as the measure of precision. The results show that the geometrical parameters have almost the same contribution in the flexural capacity and deflection of the specimens. According to the performance evaluation indexes, the best model is detected and optimized by tuning other algorithm parameters. The results indicate that the hybrid neural network can successfully predict the normalized ultimate load and deflection.

News Text Classification Based on MLCNN and BiGRU Hybrid Neural Network

Abstract: In the era of knowledge explosion, text classification is becoming increasingly crucial. At the same time, with the proposed Blockchain, it is of great research significance to actively explore the combination of Blockchain and AI, especially to apply text classification technology to the security classification of Blockchain technology. In this paper, we propose a hybrid neural network model (MLCNN & BiGRU-ATT) based on Multilayer Convolutional Neural Networks (MLCNN) and Bidirectional Gated Recurrent Unit (BiGRU) with Attention Mechanism in the news text classification field. GRU (Gate Recurrent Unit), a variant of LSTM (Long-Short Term Memory), has the natural advantages in processing time series tasks, which can readily capture the characteristics of text context information. Due to its prominent advantages in local feature extraction, CNN is also applied to NLP area, in which the researchers have made substantial progress. The experiment results reveal that our model has achieved higher accuracy on THUCNews dataset and Sougou news corpus classification.

A method for fault diagnosis in chemical reactor with hybrid neural network and genetic algorithm

Abstract: Rapid and accurate fault diagnosis remains a problem in the case of multiple fault for the large and complex chemical system. A novel evolutionary neural network for fault diagnosis is suggested. Which adopts three-layer feed — forward neural network with dual genetic algorithm (GA)loops embedded in its training. The dual GA loops are designed for optimizing both topology and connection weights of the neural network and establishing global optimal neural network for fault diagnosis. Computer simulation results in a chemical reactor indicate that the proposed evolutionary neural network fault diagnosis system works effectively and is superior to the conventional back propagation(BP)neural network.

Forecasting hybrid neural network with variational learning rate and q -DSCID synchronization evaluation for energy market

Abstract: Because of the nonlinearity, uncertainty, and dynamics of crude oil price, its price forecasting has continuously been a burdensome international research issue. To better implement the prediction of the energy market by machine learning algorithms, premeditating the influence factors of historical data in different periods on prediction consequence, random inheritance formula error correction algorithm is proposed in this work. The empirical wavelet transform and reconstruction are applied to extract data features simultaneously. A novel hybrid neural network model is constructed, which integrates empirical wavelet transform, Elman recurrent neural network, and random inheritance formula. Variational learning rate is proposed and used to ameliorate the selection of parameters for the network training procedure. In this paper, the proposed model is applied in crude oil futures price forecasting. Further, a variety of evaluation indicators are introduced to contrast and evaluate the predictions. An original representative synchronization evaluation arithmetic q-order dyadic scales complexity invariant distance is put forward and utilized. Demonstration results suggest that the proposed model has superior preciseness among comparison models.

Hybrid neural network system for cloud classification from satellite images

Abstract: This paper presents an architecture and preliminary implementation results of a hybrid two-stage neural network system for cloud classification from satellite imagery. The system first performs pixel classification on the image spectral multi-channel data and descriptive data to discover possible areas covered by clouds and cloud contaminated pixel characteristics. Then it investigates the texture of image rectangular kernels composed of classified pixels belonging to classes recorded previously with some expected to represent clouds. The system determines cloud textures, integrates pixel information from within local image areas, and provides the final cloud classification. The method is based on the unsupervised classification approach. The hybrid neural network used consists of classical and modified learning multilayer self-organizing feature maps. The preliminary tests have been made on both artificial and satellite image data. The initial results are satisfactory and promising.