Topic
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.
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01 Jul 2018
TL;DR: A theoretical analysis on the internal structure and learning method of VSF-Network, a hybrid neural network combining hierarchical neural network and the chaotic neural network, which explains from the viewpoint of the combination of the divided linear spaces that the subnetworks obtained by learning can be recognized in combination according to the situation.
Abstract: In this paper, a theoretical analysis on the internal structure and learning method of VSF-Network is introduced. It is a hybrid neural network combining hierarchical neural network and the chaotic neural network. The hierarchical neural network learns patterns and recognizes patterns based on the learning results.. The chaotic neural network monitors the internal structure of the hierarchical neural network and identifies learned units and unused units. The result of this identification is used for selection of the updating target at the time of updating the weight. With this selective weight update, the hierarchical neural network of the VSF-Network is divided into subnetworks that recognize previously learned patterns and subnetworks that recognize newly learned patterns. The monitoring by chaotic neural network is explained as calculation of eigenspace of its recall process. Furthermore, it is explained from the viewpoint of the combination of the divided linear spaces that the subnetworks obtained by learning can be recognized in combination according to the situation. Finally, the validity and problems of the theoretical explanation are introduced through the analysis of the intermediate layer of VSF-Network during incremental learning.
25 May 2022
TL;DR: Two methods based on building sequences of neural networks, as well as a hybrid neural network technology referred to as Hybrid deep neural network (HDNN) were selected.
Abstract: The paper presents the analysis of existing neural network solutions for the analysis of heterogeneous information on the example of processing data from the built-in sensors of a tablet PC. Two methods based on building sequences of neural networks, as well as a hybrid neural network technology referred to as Hybrid deep neural network (HDNN) were selected.
01 Jan 2013
TL;DR: The two hybrid neural network controllers realized the nonlinear PID control, which possessed the ability of parameters self-tuning, simple structure and are easy to implement in the practice.
Abstract: For many nonlinear factors which appear in the tracking system of Permanent Magnet Linear Synchronous Motor (PMLSM), the two hybrid controllers based on the radial basis function neural network were proposed, the hybrid neural network controllers were composed of PID controller and RBF neural network controller in series, and their structures depended on the series orders The two hybrid neural network controllers realized the nonlinear PID control, which possessed the ability of parameters self-tuning, simple structure and are easy to implement in the practice The experimental results showed the feasibility and effectiveness of the two hybrid controllers
01 Jun 2021
TL;DR: In this article, a hybrid neural network based on the vector representation of pre-formed dictionaries of terms is proposed to identify potential threats and undesirable content, in particular, using artificial intelligence (AI) methods.
Abstract: The significant volume, as well as the semantic and lexicological diversity of Internet content, necessitates the creation of new methods for its computer analysis in order to identify potential threats and undesirable content, in particular, using artificial intelligence (AI) methods. This scientific problem can be solved with the use of neural network technologies, including frequency preprocessing of text arrays, justification of the structure and construction of a domain-oriented database of text data bodies, justification and experimental study of the architecture and macroparameters of a hybrid neural network based on the vector representation of pre-formed dictionaries of terms.
08 Sep 2011
TL;DR: The proposed hybrid model outperforms the original LVQ method in average classification performance and the window size of the feature map topology was selected based on the spatial distance between the winning neuron and its neighbors automatically.
Abstract: The main motivation for the research presented in this paper is to design and to apply a framework based on a hybrid neural network model for classification of remote sensing data. The approach based on the combination of the Self-Organizing Map (SOM) and a Learning Vector Quantization (LVQ) method. Specifically, The SOM acting as a preprocessor and provides an approximate method for computing the feature map vectors of land cover over study area in an unsupervised manner. The supervised learning technique using the LVQ method with training the neural network based on a competitive learning rule, that uses class information to move the voronoi vectors slightly, so as to improve the learning time process and the quality of the classifier decision regions. Furthermore, the window size of the feature map topology was selected based on the spatial distance between the winning neuron and its neighbors automatically. The approach has been tested and verified on a Landsat-TM multispectral imagery of land cover over Belopa area, South-Sulawesi, Indonesia. Based on the experimental results, it is shown that the proposed hybrid model outperforms the original LVQ method in average classification performance.