Fuzzy clustering based self-organizing neural network for real time evaluation of wind music

2018 ◽  
Vol 52 ◽  
pp. 359-364 ◽  
Author(s):  
Tang Xiaobin
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Fuzzy Clustering ◽  
Wind Music ◽  
Time Evaluation ◽  
Self Organizing

The use of neural network and discrete Fourier transform for real-time evaluation of friction stir welding

2011 ◽  
Vol 11 (8) ◽  
pp. 4839-4846 ◽  
Author(s):  
Enkhsaikhan Boldsaikhan ◽  
Edward M. Corwin ◽  
Antonette M. Logar ◽  
William J. Arbegast
Keyword(s):  
Neural Network ◽  
Fourier Transform ◽  
Friction Stir Welding ◽  
Real Time ◽  
Discrete Fourier Transform ◽  
Friction Stir ◽  
Time Evaluation

scholarly journals Fuzzy Clustering Neural Networks for Real-Time Odor Recognition System

2007 ◽  
Vol 2007 ◽  
pp. 1-6 ◽  
Author(s):  
Bekir Karlık ◽  
Kemal Yüksek
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Real Time ◽  
Fuzzy Clustering ◽  
Input Data ◽  
Recognition System ◽  
Neural System ◽  
The Other ◽  
Odor Recognition ◽  
Recognition Probability

The aim of this study is to develop a novel fuzzy clustering neural network (FCNN) algorithm as pattern classifiers for real-time odor recognition system. In this type of FCNN, the input neurons activations are derived through fuzzy c mean clustering of the input data, so that the neural system could deal with the statistics of the measurement error directly. Then the performance of FCNN network is compared with the other network which is well-known algorithm, named multilayer perceptron (MLP), for the same odor recognition system. Experimental results show that both FCNN and MLP provided high recognition probability in determining various learn categories of odors, however, the FCNN neural system has better ability to recognize odors more than the MLP network.

scholarly journals Improving ENIGMA-style Clause Selection while Learning From History

2021 ◽  
pp. 543-561
Author(s):  
Martin Suda
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Theorem Prover ◽  
Central Idea ◽  
Theorem Provers ◽  
Recursive Neural Network ◽  
Time Evaluation ◽  
Automatic Theorem ◽  
Learning From History

AbstractWe re-examine the topic of machine-learned clause selection guidance in saturation-based theorem provers. The central idea, recently popularized by the ENIGMA system, is to learn a classifier for recognizing clauses that appeared in previously discovered proofs. In subsequent runs, clauses classified positively are prioritized for selection. We propose several improvements to this approach and experimentally confirm their viability. For the demonstration, we use a recursive neural network to classify clauses based on their derivation history and the presence or absence of automatically supplied theory axioms therein. The automatic theorem prover Vampire guided by the network achieves a 41 % improvement on a relevant subset of SMT-LIB in a real time evaluation.

ARTMAP: Supervised real-time learning and classification of nonstationary data by a self-organizing neural network

1991 ◽  
Vol 4 (5) ◽  
pp. 565-588 ◽  
Author(s):  
Gail A. Carpenter ◽  
Stephen Grossberg ◽  
John H. Reynolds
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Nonstationary Data ◽  
Self Organizing

Learning Patterns of Motion Trajectories Using Real-Time Tracking

2011 ◽  
Vol 403-408 ◽  
pp. 2768-2771
Author(s):  
Ying Feng Cai ◽  
Hai Wang ◽  
Wei Gong Zhang
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Probability Model ◽  
Activity Patterns ◽  
Visual Surveillance ◽  
Motion Trajectories ◽  
Behavior Understanding ◽  
Neural Network Algorithm ◽  
Learn Activity ◽  
Self Organizing

The understanding and description of behaviors for road vehicles is a hot topic of intelligent visual surveillance system. Trajectory analysis is one of the basic problems in behavior understanding, from which anomalies can be detected and also accidents can be predicted. In this paper, we proposed a hierarchical self-organizing neural network model to learn trajectory distribution pattern and a probability model for accident recognition. Sample data including motion trajectories are first get by real-time vehicle tracking. The self-organizing neural network algorithm is then applied to learn activity patterns from the sample trajectories. Using the learned patterns, we consider anomaly detection as well as object behavior prediction. Experiments in actual road scene show the effectiveness of the proposed algorithm.

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