Map Matching Using De-Noise Interpolation Kohonen Self-Organizing Maps

2011 ◽  
Vol 460-461 ◽  
pp. 680-686 ◽  
Author(s):  
Zhan Wei Du ◽  
Yong Jian Yang ◽  
Yong Xiong Sun ◽  
Chi Jun Zhang
Keyword(s):  
Neural Network ◽  
Large Error ◽  
Original Position ◽  
Map Matching ◽  
Self Organizing Maps ◽  
Data Set ◽  
Position Information ◽  
True Value ◽  
Match Accuracy ◽  
Self Organizing

In this work, we have proposed a de-noise interpolation Kohonen Self-Organizing Maps(DNIKSOM) -based method for the Map matching(MM). It has been seen that there are some problems in the MM, such as large error range of the original position information, low match accuracy and so on. Therefore, in MM problem to achieve high accuracy, it is necessary to consider the topography of roads and the requirement for match accuracy lying within the original position information in the matching process. In the present study, Kohonen Self-Organizing Maps(KSOM) in the field of pattern recognition possesses good performance. Now to get more valuable position information, A kind of de-noise algorithm for Kohonen neural network is proposed to meet the case that neural network may not be trained sufficiently with consideration for the topography of roads. And a kind of Lagrange interpolation algorithm is also proposed to meet the requirements for matching accuracy. These processes make the amended position information closer to the true value. In this application to a city’s MM, we investigate DNIKSOM’s ,KSOM’s and Centroid localization algorithm’s location performance on a original position data set. Finally, the comparison of experimental results shows that DNIKSOM has better location performance than others.

Machine Learning: Automated Knowledge Acquisition Based on Unsupervised Neural Network and Expert System Paradigms

2005 ◽  
Vol 9 (6) ◽  
pp. 693-697 ◽  
Author(s):  
Nazar Elfadil ◽  
Keyword(s):  
Neural Network ◽  
Expert System ◽  
Knowledge Acquisition ◽  
Training Data ◽  
Neural Network Models ◽  
Self Organizing Maps ◽  
Data Set ◽  
Unsupervised Neural Network ◽  
Automated Knowledge ◽  
Self Organizing

Self-organizing maps are unsupervised neural network models that lend themselves to the cluster analysis of high-dimensional input data. Interpreting a trained map is difficult because features responsible for specific cluster assignment are not evident from resulting map representation. This paper presents an approach to automated knowledge acquisition using Kohonen's self-organizing maps and k-means clustering. To demonstrate the architecture and validation, a data set representing animal world has been used as the training data set. The verification of the produced knowledge base is done by using conventional expert system.

A trajectory map matching algorithm via exploring the average directional features of continuous windows

2021 ◽  
pp. 1-16
Author(s):  
Xiaohan Wang ◽  
Pei Wang ◽  
Weilong Chen ◽  
Wangwu Hu ◽  
Long Yang
Keyword(s):  
Road Network ◽  
Transition Probability ◽  
Location Based Services ◽  
Original Position ◽  
Map Matching ◽  
Trajectory Data ◽  
Matching Accuracy ◽  
Data Set ◽  
Matching Algorithm ◽  
Low Efficiency

Many location-based services require a pre-processing step of map matching. Due to the error of the original position data and the complexity of the road network, the matching algorithm will have matching errors when the complex road network is implemented, which is therefore challenging. Aiming at the problems of low matching accuracy and low efficiency of existing algorithms at Y-shaped intersections and roundabouts, this paper proposes a space-time-based continuous window average direction feature trajectory map matching algorithm (STDA-matching). Specifically, the algorithm not only adaptively generates road network topology data, but also obtains more accurate road network relationships. Based on this, the transition probability is calculated by using the average direction feature of the continuous window of the track points to improve the matching accuracy of the algorithm. Secondly, the algorithm simplifies the trajectory by clustering the GPS trajectory data aggregation points to improve the matching efficiency of the algorithm. Finally, we use a real and effective data set to compare the algorithm with the two existing algorithms. Experimental results show that our algorithm is effective.

CENTROID NEURAL NETWORK WITH WEIGHTED FEATURES

2009 ◽  
Vol 18 (08) ◽  
pp. 1353-1367 ◽  
Author(s):  
DONG-CHUL PARK
Keyword(s):  
Neural Network ◽  
Image Compression ◽  
Objective Function ◽  
Lagrange Multiplier ◽  
Synthetic Data ◽  
Feature Weighting ◽  
The Self ◽  
Self Organizing Map ◽  
Data Set ◽  
Self Organizing

A Centroid Neural Network with Weighted Features (CNN-WF) is proposed and presented in this paper. The proposed CNN-WF is based on a Centroid Neural Network (CNN), an effective clustering tool that has been successfully applied to various problems. In order to evaluate the importance of each feature in a set of data, a feature weighting concept is introduced to the Centroid Neural Network in the proposed algorithm. The weight update equations for CNN-WF are derived by applying the Lagrange multiplier procedure to the objective function constructed for CNN-WF in this paper. The use of weighted features makes it possible to assess the importance of each feature and to reject features that can be considered as noise in data. Experiments on a synthetic data set and a typical image compression problem show that the proposed CNN-WF can assess the importance of each feature and the proposed CNN-WF outperforms conventional algorithms including the Self-Organizing Map (SOM) and CNN in terms of clustering accuracy.

scholarly journals Using self-organizing maps for determination of soil fertility (case study: Shiraz plain)

2018 ◽  
Vol 13 (No. 1) ◽  
pp. 11-17 ◽  
Author(s):  
M. Mokarram ◽  
M. Najafi-Ghiri ◽  
A.R. Zarei
Keyword(s):  
Neural Network ◽  
Soil Fertility ◽  
Network Models ◽  
The Other ◽  
Neural Network Models ◽  
Self Organizing Maps ◽  
Soil Features ◽  
Close Relationship ◽  
Artificial Neural Network Ann ◽  
Self Organizing

Soil fertility refers to the ability of a soil to supply plant nutrients. Naturally, micro and macro elements are made available to plants by breakdown of the mineral and organic materials in the soil. Artificial neural network (ANN) provides deeper understanding of human cognitive capabilities. Among various methods of ANN and learning an algorithm, self-organizing maps (SOM) are one of the most popular neural network models. The aim of this study was to classify the factors influencing soil fertility in Shiraz plain, southern Iran. The relationships among soil features were studied using the SOM in which, according to qualitative data, the clustering tendency of soil fertility was investigated using seven parameters (N, P, K, Fe, Zn, Mn, and Cu). The results showed that for soil fertility there is a close relationship between P and N, and also between P and Zn. The other parameters, such as K, Fe, Mn, and Cu, are not mutually related. The results showed that there are six clusters for soil fertility and also that group 1 soils are more fertile than the other.

scholarly journals Automated Construction of Neural Network Potential Energy Surface: The Enhanced Self-Organizing Incremental Neural Network Deep Potential Method

2021 ◽  
Author(s):  
Mingyuan Xu ◽  
Tong Zhu ◽  
John ZH Zhang
Keyword(s):  
Neural Network ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Potential Energy Surfaces ◽  
Energy Surface ◽  
Chemical Space ◽  
Potential Method ◽  
Dynamics Simulation ◽  
Data Set ◽  
Self Organizing

In recent years, the use of deep learning (neural network) potential energy surface (NNPES) in molecular dynamics simulation has experienced explosive growth as it can be as accurate as quantum chemistry methods while being as efficient as classical mechanic methods. However, the development of NNPES is highly non-trivial. In particular, it has been troubling to construct a dataset that is as small as possible yet can cover the target chemical space. In this work, an ESOINN-DP method is developed, which has the enhanced self-organizing incremental neural-network (ESOINN) and a newly proposed error indicator at its core. With ESOINN-DP, One can construct the NNPES with little human intervention, and this method ensures that the constructed reference dataset covers the target chemical space with minimum redundancy. The performance of the ESOINN-DP method has been well validated by developing neural network potential energy surfaces for water clusters and by de-redundancy of a sub-data set of the ANI-1 database. We believe that the ESOINN-DP method provides a novelty idea for the construction of NNPES and especially, the reference datasets, and it can be used for MD simulations of various gas-phase and condensed-phase chemical systems.

scholarly journals Score Prediction of Sports Events Based on Parallel Self-Organizing Nonlinear Neural Network

2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
Junyao Ling
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Phase Space ◽  
Development Trend ◽  
The Self ◽  
Minimum Error ◽  
Main Research ◽  
True Value ◽  
Self Organizing Networks ◽  
Self Organizing

This paper introduces the basic concepts and main characteristics of parallel self-organizing networks and analyzes and predicts parallel self-organizing networks through neural networks and their hybrid models. First, we train and describe the law and development trend of the parallel self-organizing network through historical data of the parallel self-organizing network and then use the discovered law to predict the performance of the new data and compare it with its true value. Second, this paper takes the prediction and application of chaotic parallel self-organizing networks as the main research line and neural networks as the main research method. Based on the summary and analysis of traditional neural networks, it jumps out of inertial thinking and first proposes phase space. Reconstruction parameters and neural network structure parameters are unified and optimized, and then, the idea of dividing the phase space into multiple subspaces is proposed. The multi-neural network method is adopted to track and predict the local trajectory of the chaotic attractor in the subspace with high precision to improve overall forecasting performance. During the experiment, short-term and longer-term prediction experiments were performed on the chaotic parallel self-organizing network. The results show that not only the accuracy of the simulation results is greatly improved but also the prediction performance of the real data observed in reality is also greatly improved. When predicting the parallel self-organizing network, the minimum error of the self-organizing difference model is 0.3691, and the minimum error of the self-organizing autoregressive neural network is 0.008, and neural network minimum error is 0.0081. In the parallel self-organizing network prediction of sports event scores, the errors of the above models are 0.0174, 0.0081, 0.0135, and 0.0381, respectively.

scholarly journals SYNTHESIS OF SELF-ORGANIZING MAP AND FEEDFORWARD NEURAL NETWORK FOR BETTER FORECASTING

2014 ◽  
pp. 68-75
Author(s):  
Oles Hodych ◽  
Yuriy Shcherbyna ◽  
Michael Zylan
Keyword(s):  
Neural Network ◽  
Feedforward Neural Network ◽  
Share Price ◽  
Data Sets ◽  
Self Organizing Map ◽  
Data Set ◽  
Price Fluctuation ◽  
Price Data ◽  
Comparison Results ◽  
Self Organizing

In this article the authors propose an approach to forecasting the direction of the share price fluctuation, which is based on utilization of the Feedforward Neural Network in conjunction with Self-Organizing Map. It is proposed to use the Self-Organizing Map for filtration of the share price data set, whereas the Feedforward Neural Network is used to forecast the direction of the share price fluctuation based on the filtered data set. The comparison results are presented for filtered and non-filtered share price data sets.

Ketepatan Klasifikasi Penerima Beasiswa STMIK STIKOM Bali dengan Hybrid Self Organizing Maps dan Algoritma K-Mean

Jurnal Varian ◽  
2018 ◽  
Vol 2 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Ni Putu Nanik Hendayanti ◽  
Gusti Ayu Made Arna Putri ◽  
Maulida Nurhidayati
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Artificial Neural Network ◽  
Self Organizing Maps ◽  
Cluster Group ◽  
Artificial Neural ◽  
Clustering Data ◽  
Self Organizing

Data Mining adalah penemuan informasi baru dengan mencari pola atau aturan tertentu dari sejumlah data yang sangat besar. Salah satu teknik yang dikenal dalam Data Mining yaitu clustering. Pengertian clustering dalam Data Mining adalah pengelompokan sejumlah data atau objek ke dalam cluster (group) sehingga setiap di lama cluster tersebut akan berisi data yang semirip mungkin dan berbeda dengan objek dalam cluster yang lain. Salah satu metode klasifiaksi atau clustering adalah Self Organizing Maps (SOM). SOM merupakan metode artificial neural network yang digunakan untuk mengelompokkan (clustering) data berdasarkan karakteristik/fitur-fitur data. Metode pengelompokan yang menggunakan konsep jarak dan memiliki karakteristik yang hampir sama dengan SOM yaitu metode K-means. Penelitian ini bertujuan untuk mengembangkan suatu metode yang merupakan hybrid dari SOM dan K-means yang digunakan untuk menentukan ketepatan suatu klasifikasi. Sebelum diujikan pada data asli, metode hybrid SOM dan K-Means diujikan lebih dulu pada data benchmark sehingga dapat diketahui berapa persen ketepan yang dihasilkan. Kemudian dilanjutkan dengan penerapan metode hybrid SOM dan K-means pada data penerimaan beasiswa di STMIK STIKOM Bali. Penelitian ini bertujuan untuk menentukan ketepatan klasifikasi penerima beasiswa STMIK STIKOM Bali dengan metode hybrid SOM dan K-means. Hasil penelitian menunjukkan bahwa metode Kmeans dan SOM memberikan hasil yang sama yang akibatnya metode SOM-Kmeans juga memberikan hasil yang sama. Alasannya, metode SOM-Kmeans menggunakan nilai centroid dari hasil SOM, dan hasil yang diperoleh pada metode Kmean memiliki hasil yang sama dengan SOM akibatnya metode SOM-Kmeans menghasilkan hasil yang sama dengan kedua metode sebelumnya.

Sign in / Sign up

Export Citation Format

Share Document