scholarly journals Direction of Arrival Estimation Based on DDOA and Self-Organizing Map

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Xiuhui Tan ◽  
Hongping Hu ◽  
Rong Cheng ◽  
Yanping Bai
Keyword(s):  
Sensor Array ◽  
Direction Of Arrival ◽  
Direction Of Arrival Estimation ◽  
Nonlinear Mapping ◽  
Topological Order ◽  
Self Organizing Map ◽  
Distance Difference ◽  
Som Network ◽  
Som Neural Network ◽  
Self Organizing

An effective two-level self-organizing map (SOM) neural network for direction of arrival (DOA) of sound signals estimation is proposed. The approach is based on the distance difference of arrival (DDOA) and a uniform linear sensor array in a 2D plane; it performs a nonlinear mapping between the DDOA vectors and angles of arrival (AOA). We found that the topological order of DDOA vectors and AOAs of same signals is uniform; thus, the topological order preserving of SOM network makes it valid to estimate AOA through DDOA. From the results of simulations and lake experiments, it is shown that the network has the advantage of accuracy and robustness, can be trained in advance, and is easy to implement.

scholarly journals A Novel Hardware Systolic Architecture of a Self-Organizing Map Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Khaled Ben Khalifa ◽  
Ahmed Ghazi Blaiech ◽  
Mohamed Hédi Bedoui
Keyword(s):  
Neural Network ◽  
Recent Literature ◽  
Generic Model ◽  
Network Architectures ◽  
Self Organizing Map ◽  
Modular Architecture ◽  
Systolic Architecture ◽  
Som Network ◽  
Som Neural Network ◽  
Self Organizing

In this article, we propose to design a new modular architecture for a self-organizing map (SOM) neural network. The proposed approach, called systolic-SOM (SSOM), is based on the use of a generic model inspired by a systolic movement. This model is formed by two levels of nested parallelism of neurons and connections. Thus, this solution provides a distributed set of independent computations between the processing units called neuroprocessors (NPs) which define the SSOM architecture. The NP modules have an innovative architecture compared to those proposed in the literature. Indeed, each NP performs three different tasks without requiring additional external modules. To validate our approach, we evaluate the performance of several SOM network architectures after their integration on an FPGA support. This architecture has achieved a performance almost twice as fast as that obtained in the recent literature.

Using an Extended Self-Organizing Map Network to Forecast Market Segment Membership

2011 ◽  
pp. 142-157
Author(s):  
Melody Y. Kiang ◽  
Dorothy M. Fisher ◽  
Michael Y. Hu ◽  
Robert T. Chi
Keyword(s):  
Input Data ◽  
Self Organizing Map ◽  
Market Segment ◽  
Extended Version ◽  
Extended Self ◽  
Data Set ◽  
Step Procedure ◽  
Som Network ◽  
Residential Market ◽  
Self Organizing

This chapter presents an extended Self-Organizing Map (SOM) network and demonstrates how it can be used to forecast market segment membership. The Kohonen’s SOM network is an unsupervised learning neural network that maps n-dimensional input data to a lower dimensional (usually one- or two-dimensional) output map while maintaining the original topological relations. We apply an extended version of SOM networks that further groups the nodes on the output map into a user-specified number of clusters to a residential market data set from AT&T. Specifically, the extended SOM is used to group survey respondents using their attitudes towards modes of communication. We then compare the extended SOM network solutions with a two-step procedure that uses the factor scores from factor analysis as inputs to K-means cluster analysis. Results using AT&T data indicate that the extended SOM network performs better than the two-step procedure.

Degradation assessment of ball bearings utilizing curvilinear component analysis

2018 ◽  
Vol 233 (3) ◽  
pp. 714-730 ◽  
Author(s):  
Prashant Tiwari ◽  
SH Upadhyay
Keyword(s):  
Feature Space ◽  
Component Analysis ◽  
Performance Degradation ◽  
Quantization Error ◽  
Mapping Technique ◽  
Nonlinear Mapping ◽  
Ball Bearings ◽  
Self Organizing Map ◽  
Feature Vectors ◽  
Self Organizing

The performance degradation assessment of ball bearings is of great importance to increase the efficiency and the reliability of rotating mechanical systems. The large dimensionality of feature space introduces a lot of noise and buries the potential information about faults hidden in the feature data. This paper proposes a novel health assessment method facilitated with two compatible methods, namely curvilinear component analysis and self-organizing map network. The novelty lies in the implementation of a vector quantization approach for the sub-manifolds in the feature space and to extract the fault signatures through nonlinear mapping technique. Curvilinear component analysis is a nonlinear mapping tool that can effectively represent the average manifold of the highly folded information and further preserves the local topology of the data. To answer the complications and to accomplish reliability and accuracy in bearing performance degradation assessment, the work is carried out with following steps; first, ensemble empirical mode decomposition is used to decompose the vibration signals into useful intrinsic mode functions; second, two fault features i.e. singular values and energy entropies are extracted from the envelopes of the intrinsic mode function signals; third, the extracted feature vectors under healthy conditions, further reduced with curvilinear component analysis are used to train the self-organizing map model; finally, the reduced test feature vectors are supplied to the trained self-organizing map and the confidence value is obtained. The effectiveness of the proposed technique is validated on three run-to-failure test signals with the different type of defects. The results indicate that the proposed technique detects the weak degradation earlier than the widely used indicators such as root mean square, kurtosis, self-organizing map-based minimum quantization error, and minimum quantization error-based on the principal component analysis.

New Hardware Architecture for Self-Organizing Map Used for Color Vector Quantization

2019 ◽  
Vol 29 (01) ◽  
pp. 2050002
Author(s):  
Khaled Ben Khalifa ◽  
Ahmed Ghazi Blaiech ◽  
Mehdi Abadi ◽  
Mohamed Hedi Bedoui
Keyword(s):  
Vector Quantization ◽  
Recent Literature ◽  
Network Architectures ◽  
Self Organizing Map ◽  
Description Language ◽  
Field Programmable ◽  
Color Vector ◽  
Hardware Description ◽  
Som Network ◽  
Self Organizing

In this paper, we present a new generic architectural approach of a Self-Organizing Map (SOM). The proposed architecture, called the Diagonal-SOM (D-SOM), is described as an Hardware–Description-Language as an intellectual property kernel with easily adjustable parameters.The D-SOM architecture is based on a generic formalism that exploits two levels of the nested parallelism of neurons and connections. This solution is therefore considered as a system based on the cooperation of a distributed set of independent computations. The organization and structure of these calculations process an oriented data flow in order to find a better treatment distribution between different neuroprocessors. To validate the D-SOM architecture, we evaluate the performance of several SOM network architectures after their integration on a Xilinx Virtex-7 Field Programmable Gate Array support. The proposed solution allows the easy adaptation of learning to a large number of SOM topologies without any considerable design effort. [Formula: see text] SOM hardware is validated through FPGA implementation, where temporal performance is almost twice as fast as that obtained in the recent literature. The suggested D-SOM architecture is also validated through simulation on variable-sized SOM networks applied to color vector quantization.

scholarly journals Clustering Ensemble Model Based on Self-Organizing Map Network

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Wenqi Hua ◽  
Lingfei Mo
Keyword(s):  
Poor Performance ◽  
High Dimensional ◽  
Ensemble Model ◽  
Self Organizing Map ◽  
Clustering Ensemble ◽  
The Poor ◽  
Training Samples ◽  
Cascade Structure ◽  
Som Network ◽  
Self Organizing

This paper proposes a clustering ensemble method that introduces cascade structure into the self-organizing map (SOM) to solve the problem of the poor performance of a single clusterer. Cascaded SOM is an extension of classical SOM combined with the cascaded structure. The method combines the outputs of multiple SOM networks in a cascaded manner using them as an input to another SOM network. It also utilizes the characteristic of high-dimensional data insensitivity to changes in the values of a small number of dimensions to achieve the effect of ignoring part of the SOM network error output. Since the initial parameters of the SOM network and the sample training order are randomly generated, the model does not need to provide different training samples for each SOM network to generate a differentiated SOM clusterer. After testing on several classical datasets, the experimental results show that the model can effectively improve the accuracy of pattern recognition by 4%∼10%.

A Self Organizing Map Intrusion Detection System for RPL Protocol Attacks

2019 ◽  
Vol 11 (1) ◽  
pp. 30-43 ◽  
Author(s):  
Elie Kfoury ◽  
Julien Saab ◽  
Paul Younes ◽  
Roger Achkar
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Early Stage ◽  
Sensor Data ◽  
Self Organizing Map ◽  
Lossy Networks ◽  
Mesh Topology ◽  
Som Neural Network ◽  
Self Organizing

Routing over low power and lossy networks (RPL) is a standardized routing protocol for constrained Wireless Sensor Network (WSN) environments. The main node's constraints include processing capability, power, memory, and energy. RPL protocol describes how WSN nodes create a mesh topology, enabling them to route sensor data. Unfortunately, various attacks exist on the RPL protocol that can disrupt the topology and consume nodes' energy. In this article, the authors propose an intrusion detection system (IDS) based on self-organizing map (SOM) neural network to cluster the WSN routing attacks, and hence notify the system administrator at an early stage, reducing the risk of interrupting the network and consuming nodes' power. Results showed that the proposed SOM architecture is able to cluster routing packets into three different types of attacks, as well as clean data.

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