scholarly journals Eye Movement Prediction Based on Adaptive BP Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yushou Tang ◽  
Jianhuan Su
Keyword(s):  
Neural Network ◽  
Eye Movements ◽  
Eye Movement ◽  
Bp Neural Network ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Eye Movement Tracking ◽  
Neural Network Algorithm ◽  
Real World Datasets ◽  
Eye Movements During Reading

This paper uses adaptive BP neural networks to conduct an in-depth examination of eye movements during reading and to predict reading effects. An important component for the implementation of visual tracking systems is the correct detection of eye movement using the actual data or real-world datasets. We propose the identification of three typical types of eye movements, namely, gaze, leap, and smooth navigation, using an adaptive BP neural network-based recognition algorithm for eye movement. This study assesses the BP neural network algorithm using the eye movement tracking sensors. For the experimental environment, four types of eye movement signals were acquired from 10 subjects to perform preliminary processing of the acquired signals. The experimental results demonstrate that the recognition rate of the algorithm provided in this paper can reach up to 97%, which is superior to the commonly used CNN algorithm.

Monitoring and analysis of athletes’ local body movement status based on BP neural network

2020 ◽  
pp. 1-11
Author(s):  
Ling Zhang ◽  
Faze Liang
Keyword(s):  
Neural Network ◽  
Time Domain ◽  
Bp Neural Network ◽  
Recognition Rate ◽  
The Body ◽  
Gradient Theory ◽  
Motion Recognition ◽  
Related Research ◽  
Network Algorithm ◽  
Neural Network Algorithm

At present, the body recognition detection of athletes is mostly technical recognition, and the detection of exercise state is less, and the related research is basically blank. Based on this, based on BP neural network algorithm, this study develops athletes’ motion capture based on wearable inertial sensors, and builds a wireless signal transmission scheme based on sensor system. At the same time, this paper constructs the coordinate system to complete the attitude angle settlement and motion recognition and combines the athlete’s actual situation to establish the athlete’s limb trajectory calculation model and analyzes the athletes’ movement patterns. In addition, this paper combines neural network algorithm to analyze, and builds a neural network based athlete body motion recognition model, and analyzes the model effectiveness through simulation system. Studies have shown that when using time domain features+trajectory features as neural network inputs, the hand recognition rate is somewhat improved compared to the use of only time domain features as neural network inputs. It can be seen that the algorithm model of this study has certain validity and can be used as a reference for subsequent related research gradient theory.

The Application of the Fuzzy ART Neural Network Algorithm

2014 ◽  
Vol 513-517 ◽  
pp. 1783-1786 ◽  
Author(s):  
Ming Gu
Keyword(s):  
Neural Network ◽  
Simulation Experiment ◽  
Recognition Rate ◽  
Learning Rule ◽  
Recognition Algorithm ◽  
Fuzzy Art ◽  
Fast Learning ◽  
Average Recognition Rate ◽  
Neural Network Algorithm ◽  
Better Than

An algorithm based on fuzzy ART neural network which can deal with online-learning and recognition of the known and unknown faces at the same time was designed and realized. Based on structure and learning rule of the fuzzy ART system, face recognition algorithm was designed. The simulation experiment results show that average recognition rate of not fast learning is better than fast learning. Not fast learning is accepted to get 89.83% online and 99.42% offline recognition rate.

The Recognition and Research of Improved Probabilistic Neural Network

2013 ◽  
Vol 756-759 ◽  
pp. 2819-2824
Author(s):  
Xiao Jing Shang
Keyword(s):  
Neural Network ◽  
Gesture Recognition ◽  
Probabilistic Neural Network ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Bp Network ◽  
Training Time ◽  
Neural Network Structure ◽  
Neural Network Algorithm ◽  
Better Than

Probabilistic neural network compared with the traditional BP neural network structure is simpler and it is faster to be identificated, so it is widely used in the field of pattern recognition. This paper is mainly focused on similar gesture recognition research, propose an probabilistic neural network gesture recognition algorithm. The simulation results show that the improved probabilistic neural network algorithm on the recognition rate and training time is better than the traditional BP network.

License Plate Fault-Tolerant Characters Recognition Algorithm Based on Color Segmentation and BP Neural Network

2013 ◽  
Vol 411-414 ◽  
pp. 1281-1286
Author(s):  
Xiao Chun Wang ◽  
Guo Wei Yang ◽  
Yang Yang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Fault Tolerant ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Color Segmentation ◽  
License Plate ◽  
License Plate Recognition ◽  
Shape Information ◽  
Location Algorithm

According to the license plate recognition problem, this paper did the research about license plate location and characters recognition. It proposed two new algorithms, they separately are license location algorithm based on color segmentation and fault-tolerant characters recognition algorithm based on BP neural network. In the pre-processing stage, it proposed image enhancement algorithm which could make the image more easily analyzed by computer. In the location stage, it made utilization of color and shape information, and then proposed location algorithm. In the recognition stage, it fully made the consideration of characters fault-tolerant, and then made the use of improved BP neural network to recognize characters. Experiments show that the special license plate fault-tolerant characters recognition algorithm is more accurate than the original license plate recognition methods, and its recognition rate has been improved greatly.

Age Differences in Eye Movements During Reading: Degenerative Problems or Compensatory Strategy?

2019 ◽  
Vol 24 (4) ◽  
pp. 297-311
Author(s):  
José David Moreno ◽  
José A. León ◽  
Lorena A. M. Arnal ◽  
Juan Botella
Keyword(s):  
Eye Movements ◽  
Eye Movement ◽  
Meta Analysis ◽  
Age Groups ◽  
Behavioral Changes ◽  
Aging Effects ◽  
Movement Data ◽  
Sentence Reading ◽  
The Difference ◽  
Eye Movements During Reading

Abstract. We report the results of a meta-analysis of 22 experiments comparing the eye movement data obtained from young ( Mage = 21 years) and old ( Mage = 73 years) readers. The data included six eye movement measures (mean gaze duration, mean fixation duration, total sentence reading time, mean number of fixations, mean number of regressions, and mean length of progressive saccade eye movements). Estimates were obtained of the typified mean difference, d, between the age groups in all six measures. The results showed positive combined effect size estimates in favor of the young adult group (between 0.54 and 3.66 in all measures), although the difference for the mean number of fixations was not significant. Young adults make in a systematic way, shorter gazes, fewer regressions, and shorter saccadic movements during reading than older adults, and they also read faster. The meta-analysis results confirm statistically the most common patterns observed in previous research; therefore, eye movements seem to be a useful tool to measure behavioral changes due to the aging process. Moreover, these results do not allow us to discard either of the two main hypotheses assessed for explaining the observed aging effects, namely neural degenerative problems and the adoption of compensatory strategies.

Research of Fault Diagnosis of Belt Conveyor Based on Fuzzy Neural Network

2014 ◽  
Vol 8 (1) ◽  
pp. 916-921
Author(s):  
Yuan Yuan ◽  
Wenjun Meng ◽  
Xiaoxia Sun
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Fault Location ◽  
Fuzzy Neural Network ◽  
Fuzzy Theory ◽  
Belt Conveyor ◽  
Fuzzy Neural ◽  
Neural Network Algorithm ◽  
Network Fault

To address deficiencies in the process of fault diagnosis of belt conveyor, this study uses a BP neural network algorithm combined with fuzzy theory to provide an intelligent fault diagnosis method for belt conveyor and to establish a BP neural network fault diagnosis model with a predictive function. Matlab is used to simulate the fuzzy BP neural network fault diagnosis of the belt conveyor. Results show that the fuzzy neural network can filter out unnecessary information; save time and space; and improve the fault diagnosis recognition, classification, and fault location capabilities of belt conveyor. The proposed model has high practical value for engineering.

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