An incremental online semi-supervised active learning algorithm based on self-organizing incremental neural network

2010 ◽  
Vol 20 (7) ◽  
pp. 1061-1074 ◽  
Author(s):  
Furao Shen ◽  
Hui Yu ◽  
Keisuke Sakurai ◽  
Osamu Hasegawa
Keyword(s):  
Neural Network ◽  
Active Learning ◽  
Learning Algorithm ◽  
Self Organizing

Faster Self-Organizing Fuzzy Neural Network Training and Improved Autonomy with Time-Delayed Synapses for Locally Recurrent Learning

2011 ◽  
pp. 156-183 ◽  
Author(s):  
Damien Coyle ◽  
Girijesh Prasad ◽  
Martin McGinnity
Keyword(s):  
Neural Network ◽  
Network Structure ◽  
Computational Efficiency ◽  
Fuzzy Neural Network ◽  
Learning Algorithm ◽  
Feedback Connection ◽  
Structure Changes ◽  
Fuzzy Neural ◽  
Output Behavior ◽  
Self Organizing

This chapter describes a number of modifications to the learning algorithm and architecture of the self-organizing fuzzy neural network (SOFNN) to improve its computational efficiency and learning ability. To improve the SOFNN’s computational efficiency, a new method of checking the network structure after it has been modified is proposed. Instead of testing the entire structure every time it has been modified, a record is kept of each neuron’s firing strength for all data previously clustered by the network. This record is updated as training progresses and is used to reduce the computational load of checking network structure changes, to ensure performance degradation does not occur, resulting in significantly reduced training times. It is shown that the modified SOFNN compares favorably to other evolving fuzzy systems in terms of accuracy and structural complexity. In addition, a new architecture of the SOFNN is proposed where recurrent feedback connections are added to neurons in layer three of the structure. Recurrent connections allow the network to learn the temporal information from data and, in contrast to pure feed forward architectures which exhibit static input-output behavior in advance, recurrent models are able to store information from the past (e.g., past measurements of the time-series) and are therefore better suited to analyzing dynamic systems. Each recurrent feedback connection includes a weight which must be learned. In this work a learning approach is proposed where the recurrent feedback weight is updated online (not iteratively) and proportional to the aggregate firing activity of each fuzzy neuron. It is shown that this modification can significantly improve the performance of the SOFNN’s prediction capacity under certain constraints.

Dynamic Niche-Based Self-Organizing Learning Algorithm

2011 ◽  
Vol 22 (8) ◽  
pp. 1738-1748 ◽  
Author(s):  
Chuan-Hua ZHOU ◽  
An-Shi XIE
Keyword(s):  
Learning Algorithm ◽  
Self Organizing

Share Market Data Prediction Strategies using Deep Learning Algorithm

2019 ◽  
Vol 13 ◽  
Author(s):  
A John. ◽  
D. Praveen Dominic ◽  
M. Adimoolam ◽  
N. M. Balamurugan
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Stock Market ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Market Price ◽  
Stochastic Gradient ◽  
Stochastic Gradient Descent ◽  
Mining Machine ◽  
Gradient Descent Algorithm

Background:: Predictive analytics has a multiplicity of statistical schemes from predictive modelling, data mining, machine learning. It scrutinizes present and chronological data to make predictions about expectations or if not unexplained measures. Most predictive models are used for business analytics to overcome loses and profit gaining. Predictive analytics is used to exploit the pattern in old and historical data. Objective: People used to follow some strategies for predicting stock value to invest in the more profit-gaining stocks and those strategies to search the stock market prices which are incorporated in some intelligent methods and tools. Such strategies will increase the investor’s profits and also minimize their risks. So prediction plays a vital role in stock market gaining and is also a very intricate and challenging process. Method: The proposed optimized strategies are the Deep Neural Network with Stochastic Gradient for stock prediction. The Neural Network is trained using Back-propagation neural networks algorithm and stochastic gradient descent algorithm as optimal strategies. Results: The experiment is conducted for stock market price prediction using python language with the visual package. In this experiment RELIANCE.NS, TATAMOTORS.NS, and TATAGLOBAL.NS dataset are taken as input dataset and it is downloaded from National Stock Exchange site. The artificial neural network component including Deep Learning model is most effective for more than 100,000 data points to train this model. This proposed model is developed on daily prices of stock market price to understand how to build model with better performance than existing national exchange method.

scholarly journals An Intelligent Evaluation Method to Analyze the Competitiveness of Airlines

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Jun Zhao ◽  
Xumei Chen
Keyword(s):  
Neural Network ◽  
Prior Knowledge ◽  
Evaluation Method ◽  
Standard Sample ◽  
Reference Value ◽  
The Self ◽  
Index Data ◽  
Som Neural Network ◽  
Simulation Results ◽  
Self Organizing

An intelligent evaluation method is presented to analyze the competitiveness of airlines. From the perspective of safety, service, and normality, we establish the competitiveness indexes of traffic rights and the standard sample base. The self-organizing mapping (SOM) neural network is utilized to self-organize and self-learn the samples in the state of no supervision and prior knowledge. The training steps of high convergence speed and high clustering accuracy are determined based on the multistep setting. The typical airlines index data are utilized to verify the effect of the self-organizing mapping neural network on the airline competitiveness analysis. The simulation results show that the self-organizing mapping neural network can accurately and effectively classify and evaluate the competitiveness of airlines, and the results have important reference value for the allocation of traffic rights resources.

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