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 and expert systems

Abstract: Preface. Part I: Fundamentals of Hybrid Systems. 1. Overview of Neural and Symbolic Systems. 2. Research in Hybrid Neural and Symbolic Systems. 3. Models for Integrating Systems. Part II: Case Studies of Hybrid Neural Network and Expert Systems. 4. LAM Hybrid System for Window Glazing Design. 5. Hybrid Systems Approach to Nuclear Plant Monitoring. 6. Chemical Tank Control System. 7. Image Interpretation via Fusion of Heterogeneous Sources using a Hybrid Expert-Neural Network System. 8. Hybrid Systems for Multiple Target Recognition. Part III: Analysis and Guidelines. 9. Guidelines for Developing Hybrid Systems. 10. Tools and Development Systems. 11. Summary and the Future of Hybrid Neural Network and Expert Systems. References. Index.

Classifying Wheat Hyperspectral Pixels of Healthy Heads and Fusarium Head Blight Disease Using a Deep Neural Network in the Wild Field

Abstract: Classification of healthy and diseased wheat heads in a rapid and non-destructive manner for the early diagnosis of Fusarium head blight disease research is difficult. Our work applies a deep neural network classification algorithm to the pixels of hyperspectral image to accurately discern the disease area. The spectra of hyperspectral image pixels in a manually selected region of interest are preprocessed via mean removal to eliminate interference, due to the time interval and the environment. The generalization of the classification model is considered, and two improvements are made to the model framework. First, the pixel spectra data are reshaped into a two-dimensional data structure for the input layer of a Convolutional Neural Network (CNN). After training two types of CNNs, the assessment shows that a two-dimensional CNN model is more efficient than a one-dimensional CNN. Second, a hybrid neural network with a convolutional layer and bidirectional recurrent layer is reconstructed to improve the generalization of the model. When considering the characteristics of the dataset and models, the confusion matrices that are based on the testing dataset indicate that the classification model is effective for background and disease classification of hyperspectral image pixels. The results of the model show that the two-dimensional convolutional bidirectional gated recurrent unit neural network (2D-CNN-BidGRU) has an F1 score and accuracy of 0.75 and 0.743, respectively, for the total testing dataset. A comparison of all the models shows that the hybrid neural network of 2D-CNN-BidGRU is the best at preventing over-fitting and optimize the generalization. Our results illustrate that the hybrid structure deep neural network is an excellent classification algorithm for healthy and Fusarium head blight diseased classification in the field of hyperspectral imagery.

Load Forecasting Using Hybrid Models

Abstract: This paper presents two hybrid neural networks derived from fuzzy neural networks (FNN): wavelet fuzzy neural network (WFNN) using the fuzzified wavelet features as the inputs to FNN and fuzzy neural network (FNCI) employing the Choquet integral as the outputs of FNN. The learning through FNCI is simplified by the use of q-measure and the speed of convergence of the parameters is increased by reinforced learning. The underlying fuzzy models of these hybrid networks are a modified form of fuzzy rules of Takagi-Sugeno model. The number of fuzzy rules is found from a fuzzy curve corresponding to each input-output by counting the total number of peaks and troughs in the curve. The models can forecast hourly load with a lead time of 1 h as they deal with short-term load forecasting. The results of the two hybrid networks using Indian utility data are compared with ANFIS and other conventional methods. The performance of the proposed WFNN is found superior to all the other compared methods.

Hybrid neural network modeling of a full-scale industrial wastewater treatment process.

Abstract: In recent years, hybrid neural network approaches, which combine mechanistic and neural network models, have received considerable attention. These approaches are potentially very efficient for obtaining more accurate predictions of process dynamics by combining mechanistic and neural network models in such a way that the neural network model properly accounts for unknown and nonlinear parts of the mechanistic model. In this work, a full-scale coke-plant wastewater treatment process was chosen as a model system. Initially, a process data analysis was performed on the actual operational data by using principal component analysis. Next, a simplified mechanistic model and a neural network model were developed based on the specific process knowledge and the operational data of the coke-plant wastewater treatment process, respectively. Finally, the neural network was incorporated into the mechanistic model in both parallel and serial configurations. Simulation results showed that the parallel hybrid modeling approach achieved much more accurate predictions with good extrapolation properties as compared with the other modeling approaches even in the case of process upset caused by, for example, shock loading of toxic compounds. These results indicate that the parallel hybrid neural modeling approach is a useful tool for accurate and cost-effective modeling of biochemical processes, in the absence of other reasonably accurate process models.

Improved Deep Hybrid Networks for Urban Traffic Flow Prediction Using Trajectory Data

Abstract: The urban traffic flow prediction is a significant issue in the intelligent transportation system. In consideration of nonlinear and spatial-temporal features of urban traffic data, we propose a deep hybrid neural network improved by greedy algorithm for urban traffic flow prediction with taxi GPS trace. The proposed deep neural network model first combines the convolutional neural network (CNN), which extracts the spatial features, with the long short term memory (LSTM), which captures the temporal information, to predict urban traffic flow. Then, the proposed model is trained by a greedy policy to short time consumption and improves accuracy when a network goes deeper. Experimental results with real taxis GPS trajectory data from ${Xi'an}$ city show that the improved deep hybrid CNN-LSTM model can achieve higher prediction accuracy and shorter time consumption compared with existing methods.