An Optimizing Method of Competitive Neural Network

2011 ◽  
Vol 467-469 ◽  
pp. 894-899
Author(s):  
Hong Men ◽  
Hai Yan Liu ◽  
Lei Wang ◽  
Yun Peng Pan
Keyword(s):  
Neural Network ◽  
Network Structure ◽  
Clustering Analysis ◽  
Learning Rate ◽  
Optimum Number ◽  
Number Of Clusters ◽  
Competitive Neural Network ◽  
Output Neurons ◽  
Simulation Results ◽  
Strong Ability

This paper presents an optimizing method of competitive neural network(CNN):During clustering analysis fixed on the optimum number of output neurons according to the change of DB value，and then adjusted connected weight including increasing ,dividing , delete. Each neuron had the different variety trend of learning rate according with the change of the probability of neurons. The optimizing method made classification more accurate. Simulation results showed that optimized network structure had a strong ability to adjust the number of clusters dynamically and good results of classification.

scholarly journals Algorithm for determining the optimum number of clusters on the basis of the Kohonen neural network

Connectivity ◽  
2020 ◽  
Vol 145 (3) ◽  
Author(s):  
O. M. Tkachenko ◽  
◽  
N. V. Rudenko ◽  
S. R. Kufterina ◽  
A. V. Lemeshko ◽  
...  
Keyword(s):  
Neural Network ◽  
Experimental Studies ◽  
Optimal Number ◽  
Multidimensional Data ◽  
Optimum Number ◽  
Self Organizing Maps ◽  
Number Of Clusters ◽  
General Sequence ◽  
Kohonen Neural Network ◽  
Self Organizing

The article discusses the possibilities of using artificial intelligence systems to solve clustering problems. The value of the optimality criterion for various combinations of the number of clusters and the number of neurons of the output network layer is determined. Self-organizing maps (SOM, Self Organizing Maps), developed by T. Kohonen and representing a powerful tool combining two important paradigms of data analysis - clustering and projecting, visualization of multidimensional data on a plane are considered. An example of the location of cluster nuclei after training the Kohonen neural network for different values of the number of neurons in the source layer is given. Comparing the speed of modern computers with the speed of the Kohonen neural network, with other types of neural networks, allows you to conduct a large number of network exercises in a short time, so you can use one of many methods to determine the maximum value of the function. The results of experimental studies to determine the criterion of optimality are presented in the article for different combinations of the number of clusters and the number of neurons in the original layer of the network. According to the method at the initial stage, a set of input vectors is formed, each of which includes three values. A general sequence of actions is formulated to calculate the optimal number of neurons in the output layer of the Kohonen network. The methodology presented in the article is a further development of teaching methods without a teacher. The technique proposed in the article avoids the need to specify the number of outputs of the Kohonen neural network and can be widely used both in solving data mining problems and in recognizing new unknown classes and situations in different fields.

scholarly journals Oil Family Typing Using a Hybrid Model of Self-Organizing Map and Artificial Neural Network

2021 ◽  
Author(s):  
Amir Mosavi ◽  
Majid
Keyword(s):  
Neural Network ◽  
Present Report ◽  
Principal Component ◽  
Optimum Number ◽  
Self Organizing Map ◽  
Number Of Clusters ◽  
Validity Indices ◽  
Artificial Neural ◽  
Migration Pathways ◽  
Clustering Validity

Identifying the number of oil families in petroleum basins provides practical and valuable information in petroleum geochemistry studies from exploration to development. Oil family grouping helps us track migration pathways, identify the number of active source rock(s), and examine the reservoir continuity. To date, almost in all oil family typing studies, common statistical methods such as principal component analysis (PCA) and hierarchical clustering analysis (HCA) have been used. However, there is no publication regarding using artificial neural networks (ANNs) for examining the oil families in petroleum basins. Hence, oil family typing requires novel, not overused and common techniques. This paper is the first report of oil family typing using ANNs as robust computational methods. To this end, a self-organization map (SOM) neural network associated with three clustering validity indices were employed on oil samples belonging to the Iranian part of the Persian Gulf’ oilfields. For the SOM network, at first, ten default clusters were selected. Afterwards, three effective clustering validity coefficients, namely Calinski-Harabasz (CH), Silhouette indexes (SI) and Davies-Bouldin (DB), were operated to find the optimum number of clusters. Accordingly, among ten default clusters, the maximum CH (62) and SI (0.58) were acquired for four clusters. Likewise, the lowest DB (0.8) was obtained for four clusters. Thus, all three validation coefficients introduced four clusters as the optimum number of clusters or oil families. The number of oil families identified in the present report is consistent with those previously reported by other researchers in the same study area. However, the techniques used in the present paper, which have not been implemented so far, can be introduced as more straightforward for clustering purposes in the oil family typing than those of common and overused methods of PCA and HCA.

scholarly journals Oil Family Typing Using a Hybrid Model of Self-Organizing Map and Artificial Neural Network

2021 ◽  
Author(s):  
Majid ◽  
Amir Mosavi
Keyword(s):  
Neural Network ◽  
Present Report ◽  
Principal Component ◽  
Optimum Number ◽  
Self Organizing Map ◽  
Number Of Clusters ◽  
Validity Indices ◽  
Artificial Neural ◽  
Migration Pathways ◽  
Clustering Validity

Identifying the number of oil families in petroleum basins provides practical and valuable information in petroleum geochemistry studies from exploration to development. Oil family grouping helps us track migration pathways, identify the number of active source rock(s), and examine the reservoir continuity. To date, almost in all oil family typing studies, common statistical methods such as principal component analysis (PCA) and hierarchical clustering analysis (HCA) have been used. However, there is no publication regarding using artificial neural networks (ANNs) for examining the oil families in petroleum basins. Hence, oil family typing requires novel, not overused and common techniques. This paper is the first report of oil family typing using ANNs as robust computational methods. To this end, a self-organization map (SOM) neural network associated with three clustering validity indices were employed on oil samples belonging to the Iranian part of the Persian Gulf’ oilfields. For the SOM network, at first, ten default clusters were selected. Afterwards, three effective clustering validity coefficients, namely Calinski-Harabasz (CH), Silhouette indexes (SI) and Davies-Bouldin (DB), were operated to find the optimum number of clusters. Accordingly, among ten default clusters, the maximum CH (62) and SI (0.58) were acquired for four clusters. Likewise, the lowest DB (0.8) was obtained for four clusters. Thus, all three validation coefficients introduced four clusters as the optimum number of clusters or oil families. The number of oil families identified in the present report is consistent with those previously reported by other researchers in the same study area. However, the techniques used in the present paper, which have not been implemented so far, can be introduced as more straightforward for clustering purposes in the oil family typing than those of common and overused methods of PCA and HCA.

Deadlift Recognition and Application based on Multiple Modalities using Recurrent Neural Network

2020 ◽  
Vol 2020 (17) ◽  
pp. 2-1-2-6
Author(s):  
Shih-Wei Sun ◽  
Ting-Chen Mou ◽  
Pao-Chi Chang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Structure ◽  
Inertial Sensors ◽  
Body Movement ◽  
The Body ◽  
Body Parts ◽  
Multimodal Sensing ◽  
Multiple Devices ◽  
Neural Network Structure

To improve the workout efficiency and to provide the body movement suggestions to users in a “smart gym” environment, we propose to use a depth camera for capturing a user’s body parts and mount multiple inertial sensors on the body parts of a user to generate deadlift behavior models generated by a recurrent neural network structure. The contribution of this paper is trifold: 1) The multimodal sensing signals obtained from multiple devices are fused for generating the deadlift behavior classifiers, 2) the recurrent neural network structure can analyze the information from the synchronized skeletal and inertial sensing data, and 3) a Vaplab dataset is generated for evaluating the deadlift behaviors recognizing capability in the proposed method.

SIMULASI NEURAL NETWORK UNTUK PREDIKSI LOKASI USER PADA MOBILE NETWORK

2016 ◽  
Vol 9 (1) ◽  
Author(s):  
Alexander Driyarkoro ◽  
Nurain Silalahi ◽  
Joko Haryatno
Keyword(s):  
Neural Network ◽  
Mobile Station ◽  
Mobile Network ◽  
Learning Rate ◽  
Noise Factor ◽  
Optimal Learning ◽  
Air Interface ◽  
Location Area ◽  
Optimal Learning Rate

Prediksi lokasi user pada mobile network merupakan hal sangat penting, karena routing panggilan pada mobile station (MS) bergantung pada posisi MS saat itu. Mobilitas MS yang cukup tinggi, terutama di daerah perkotaan, menyebabkan pencarian (tracking) MS akan berpengaruh pada kinerja sistem mobile network, khususnya dalam hal efisiensi kanal kontrol pada air interface. Salah satu bentuk pencarian adalah dengan mengetahui perilaku gerakan yang menentukan posisi MS. Dari MSC/VLR dapat diketahui posisi MS pada waktu tertentu. Karena location area suatu MS selalu unik dari waktu ke waktu, dan hal itu merupakan perilaku (behaviour) MS, maka dapat dibuat profil pergerakannya. Dengan menggunakan Neural Network (NN) akan diperoleh location area MS pada masa yang akan datang. Model NN yang digunakan pada penelitian ini adalah Propagasi Balik. Beberapa parameter NN yang diteliti dalam mempengaruhi kinerja prediksi lokasi user meliputi noise factor, momentum dan learning rate. Pada penelitian ini diperoleh nilai optimal learning rate = 0,5 dan noise factor = 1.

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