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.

HNN: A Novel Model to Study the Intrusion Detection Based on Multi-feature Correlation and Temporal-spatial Analysis

Abstract: Network intrusion poses a severe threat to the Internet. Intrusion detection methods based on deep learning are very effective to process and analyze intrusion data. On the one hand, they use the Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) models to extract spatial and temporal features, respectively. However, they either adopt a single model or operate two models in series. And it fails to capture temporal-spatial features effectively. On the other hand, previous methods do not consider the multi-feature correlation of intrusion data. And then they cannot get better classification performance. To address the two above problems, we propose a hybrid neural network (HNN) model by integrating multi-feature correlation and temporal-spatial analysis. First, we adopt a contribution-based feature selection. Second, we reconstruct multi-feature correlation and then apply the CNN and LSTM in parallel to extract temporal-spatial features. Finally, we splice the temporal-spatial features with the correlation features, or study the influence of the temporal-spatial features by attention mechanism. Based on the above operations, we exploit the Deep Neural Network (DNN) to detect intrusion data. The experimental results show that HNN improves 3.78%, 1.31%, 0.21%, and 1.13% accuracy on the UNSW-NB15, AWID, CICIDS 2017, and CICIDS 2018 datasets.

Isolated word recognition using a hybrid neural network

Abstract: A hybrid neural network is described. It consists of a Kohonen map and a perceptron. The hybrid is proposed firstly for speaker independent, isolated word recognition. However, it may also be used for other classification problems. The novel idea in this system is the usage of a Kohonen map as the feature extractor which converts phonetic similarities of the speech frames into spatial adjacency in the map. This property simplifies the classification task. The system performance was evaluated for recognition of a limited number of Farsi words (numbers "zero" through "ten"). The overall performance of the recognizer showed to be 93.82%. >

Content-based hierarchical document organization using multi-layer hybrid network and tree-structured features

Abstract: Automatic organizing documents through a hierarchical tree is demanding in many real applications. In this work, we focus on the problem of content-based document organization through a hierarchical tree which can be viewed as a classification problem. We proposed a new document representation to enhance the classification accuracy. We developed a new hybrid neural network model to handle the new document representation. In our document representation, a document is represented by a tree-structure that has a superior capability of encoding document characteristics. Compared to traditional feature representation that encodes only global characteristics of a document, the proposed approach can encode both global and local characteristics of a document through a hierarchical tree. Unlike traditional representation, the tree representation reflects the spatial organizations of words through pages and paragraphs of a document that help to encode better semantics of a document. Processing hierarchical tree is another challenging task in terms of computational complexity. We developed a hybrid neural network model, composed of SOM and MLP, for this task. Experimental results corroborate that our approach is efficient and effective in registering documents into organized tree compared with other approach.

A hybrid neural network---fuzzy logic approach to nuclear power plant transient identification

Abstract: A methodology is presented that couples pretrained artificial neural networks (ANNs) to rule-based fuzzy logic systems, for the purpose of distinguishing different transients in a Nuclear Power Plant (NPP). A model referenced approach is utilized in order to provide timely concise and task specific information about the status of the system under consideration. A rule based system integrated with a set of neural networks, that typify steady-state operation as well as different transients, diagnoses the state of the system and identifies the type of transient under development. ANNs produce their response in the form of membership functions which independently represent individual transients and the steady-state. Membership functions condense functionally relevant information in order for the overall system to successfully perform transient identification, in a time span faster or at least comparable to that of the transient development. To demonstrate the proposed methodology simulated accidents corresponding to a particular category of transients are used. The results obtained demonstrate the excellent noise tolerance of the ANNs and suggest a new approach for transient identification within the framework of fuzzy logic.

Dynamic Modeling and Optimal Control of Batch Reactors, Based on Structure Approaching Hybrid Neural Networks

Abstract: A novel Structure Approaching Hybrid Neural Network (SAHNN) approach to model batch reactors is presented. The Virtual Supervisor−Artificial Immune Algorithm method is utilized for the training of SAHNN, especially for the batch processes with partial unmeasurable state variables. SAHNN involves the use of approximate mechanistic equations to characterize unmeasured state variables. Since the main interest in batch process operation is on the end-of-batch product quality, an extended integral square error control index based on the SAHNN model is applied to track the desired temperature profile of a batch process. This approach introduces model mismatches and unmeasured disturbances into the optimal control strategy and provides a feedback channel for control. The performance of robustness and antidisturbances of the control system are then enhanced. The simulation result indicates that the SAHNN model and model-based optimal control strategy of the batch process are effective.