Distributed neural network self-learning algorithm

2015 ◽  
pp. 351-354
Keyword(s):  
Neural Network ◽  
Learning Algorithm ◽  
Self Learning

Fault Diagnosis of Marine Diesel Engines Based on SOM Neural Network

2011 ◽  
Vol 219-220 ◽  
pp. 809-813 ◽  
Author(s):  
Qiang Song ◽  
Ai Min Wang
Keyword(s):  
Neural Network ◽  
Diesel Engines ◽  
Learning Algorithm ◽  
Learning Network ◽  
Marine Diesel ◽  
Som Neural Network ◽  
The Cost ◽  
Fully Connected ◽  
High Degree ◽  
Self Learning

SOM neural network is a fully connected array of neurons composed of non-teachers and self-learning network, which has a strong nonlinear mapping ability and flexible network structure and a high degree of fault tolerance and robustness. This paper introduces the structure of SOM neural network and learning algorithm and presents an instance of marine diesel engines in MATLAB environment. The diagnosis of marine diesel engine showed that the model can reduce the cost of diagnosis and increase the efficiency of diagnosis. There will be well application prospect in practice.

A Multilayered Self-Learning Spiking Neural Network and its Learning Algorithm Based on ‘Winner-Takes-More’ Rule in Hierarchical Clustering

2009 ◽  
Vol 40 (1) ◽  
pp. 66-74 ◽  
Author(s):  
Yevgeniy Bodyanskiy ◽  
Artem Dolotov
Keyword(s):  
Neural Network ◽  
Systems Theory ◽  
Control Systems ◽  
Hierarchical Clustering ◽  
Learning Algorithm ◽  
Population Coding ◽  
Spiking Neural Network ◽  
Spiking Neuron ◽  
Control Systems Theory ◽  
Self Learning

A Multilayered Self-Learning Spiking Neural Network and its Learning Algorithm Based on ‘Winner-Takes-More’ Rule in Hierarchical ClusteringThis paper introduces architecture of multilayered selflearning spiking neural network for hierarchical data clustering. It consists of the layer of population coding and several layers of spiking neurons. Contrary to originally suggested multilayered spiking neural network, the proposed one does not require a separate learning algorithm for lateral connections. Irregular clusters detecting capability is achieved by improving the temporal Hebbian learning algorithm. It is generalized by replacing ‘Winner-Takes-All’ rule with ‘Winner-Takes-More’ one. It is shown that the layer of receptive neurons can be treated as a fuzzification layer where pool of receptive neurons is a linguistic variable, and receptive neuron within a pool is a linguistic term. The network architecture is designed in terms of control systems theory. Using the Laplace transform notion, spiking neuron synapse is presented as a second-order critically damped response unit. Spiking neuron soma is modeled on the basis of bang-bang control systems theory as a threshold detection system. Simulation experiment confirms that the proposed architecture is effective in detecting irregular clusters.

scholarly journals Self Learning Gaming Bot using CNN.

2020 ◽  
Vol 9 (4) ◽  
pp. 2397-2401
Keyword(s):  
Neural Network ◽  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Intelligent Agent ◽  
Maximum Score ◽  
Input Method ◽  
Present Universe ◽  
High Level ◽  
Fully Connected ◽  
Self Learning

In the present universe of current gaming condition bots are the intelligent agent that assumes a prominent job in the popularity of a game in the market. As these bots have gotten very unsurprising to the games. So here we are proposed an AI model for playing games with high level inputs using reinforcement learning. Algorithm works in the Atari Environment i.e. we are using 2D game. This model consists of the CNN (convolution neural network) for the inputs which is fully connected layers and find out the actions according to the inputs. In this learning -based approach, bots learned how to attack and ignore opponents so that bot can get maximum score. In this learning -based approach, bots learned how to attack and ignore opponents so that bot can get maximum score Then we tried the combine the input method which results maximum score of the bot in the environment for the better performance.

Operon Prediction Based On an Iterative Self-learning Algorithm*

2011 ◽  
Vol 38 (7) ◽  
pp. 642-651
Author(s):  
Wen-Qi Wu ◽  
Xiao-Bin ZHENG ◽  
Yong-Chu LIU ◽  
Kai TANG ◽  
Huai-Qiu ZHU
Keyword(s):  
Learning Algorithm ◽  
Operon Prediction ◽  
Self Learning

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 Deep Learning Algorithm Trained with COVID-19 Pneumonia Also Identifies Immune Checkpoint Inhibitor Therapy-Related Pneumonitis

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 652 ◽  
Author(s):  
Carlo Augusto Mallio ◽  
Andrea Napolitano ◽  
Gennaro Castiello ◽  
Francesco Maria Giordano ◽  
Pasquale D'Alessio ◽  
...  
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitor ◽  
Learning Algorithm ◽  
Checkpoint Inhibitor ◽  
Deep Convolutional Neural Network ◽  
Deep Learning Algorithm

Background: Coronavirus disease 2019 (COVID-19) pneumonia and immune checkpoint inhibitor (ICI) therapy-related pneumonitis share common features. The aim of this study was to determine on chest computed tomography (CT) images whether a deep convolutional neural network algorithm is able to solve the challenge of differential diagnosis between COVID-19 pneumonia and ICI therapy-related pneumonitis. Methods: We enrolled three groups: a pneumonia-free group (n = 30), a COVID-19 group (n = 34), and a group of patients with ICI therapy-related pneumonitis (n = 21). Computed tomography images were analyzed with an artificial intelligence (AI) algorithm based on a deep convolutional neural network structure. Statistical analysis included the Mann–Whitney U test (significance threshold at p < 0.05) and the receiver operating characteristic curve (ROC curve). Results: The algorithm showed low specificity in distinguishing COVID-19 from ICI therapy-related pneumonitis (sensitivity 97.1%, specificity 14.3%, area under the curve (AUC) = 0.62). ICI therapy-related pneumonitis was identified by the AI when compared to pneumonia-free controls (sensitivity = 85.7%, specificity 100%, AUC = 0.97). Conclusions: The deep learning algorithm is not able to distinguish between COVID-19 pneumonia and ICI therapy-related pneumonitis. Awareness must be increased among clinicians about imaging similarities between COVID-19 and ICI therapy-related pneumonitis. ICI therapy-related pneumonitis can be applied as a challenge population for cross-validation to test the robustness of AI models used to analyze interstitial pneumonias of variable etiology.

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