scholarly journals Canoeing Motion Tracking and Analysis via Multi-Sensors Fusion

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2110 ◽  
Author(s):  
Long Liu ◽  
Sen Qiu ◽  
ZheLong Wang ◽  
Jie Li ◽  
JiaXin Wang
Keyword(s):  
Motion Tracking ◽  
Learning Algorithm ◽  
Inertial Sensor ◽  
Recovery Phase ◽  
Descent Method ◽  
Sensor Nodes ◽  
Sensor Calibration ◽  
Gradient Descent Method ◽  
Whole Process ◽  
Training Methodologies

Coaches and athletes are constantly seeking novel training methodologies in an attempt to improve athletic performance. This paper proposes a method of rowing sport capture and analysis based on Inertial Measurement Units (IMUs). A canoeist’s motion was collected by multiple miniature inertial sensor nodes. The gradient descent method was used to fuse data and obtain the canoeist’s attitude information after sensor calibration, and then the motions of canoeist’s actions were reconstructed. Stroke quality was performed based on the estimated joint angles. Machine learning algorithm was used as the classification method to divide the stroke cycle into different phases, including propulsion-phase and recovery-phase, a quantitative kinematic analysis was carried out. Experiments conducted in this paper demonstrated that our method possesses the capacity to reveal the similarities and differences between novice and coach, the whole process of canoeist’s motions can be analyzed with satisfactory accuracy validated by videography method. It can provide quantitative data for coaches or athletes, which can be used to improve the skills of rowers.

Meteorological Data Forecast using RNN

2020 ◽  
pp. 905-920
Author(s):  
Stefan Balluff ◽  
Jörg Bendfeld ◽  
Stefan Krauter
Keyword(s):  
Neural Networks ◽  
Wind Speed ◽  
Linear Prediction ◽  
Learning Algorithm ◽  
Meteorological Data ◽  
System Modeling ◽  
Descent Method ◽  
Gradient Descent Method ◽  
Earth System Modeling ◽  
Set Up

Gathering knowledge not only of the current but also the upcoming wind speed is getting more and more important as the experience of operating and maintaining wind turbines is increasing. Not only with regards to operation and maintenance tasks such as gearbox and generator checks but moreover due to the fact that energy providers have to sell the right amount of their converted energy at the European energy markets, the knowledge of the wind and hence electrical power of the next day is of key importance. Selling more energy as has been offered is penalized as well as offering less energy as contractually promised. In addition to that the price per offered kWh decreases in case of a surplus of energy. Achieving a forecast there are various methods in computer science: fuzzy logic, linear prediction or neural networks. This paper presents current results of wind speed forecasts using recurrent neural networks (RNN) and the gradient descent method plus a backpropagation learning algorithm. Data used has been extracted from NASA's Modern Era-Retrospective analysis for Research and Applications (MERRA) which is calculated by a GEOS-5 Earth System Modeling and Data Assimilation system. The presented results show that wind speed data can be forecasted using historical data for training the RNN. Nevertheless, the current set up system lacks robustness and can be improved further with regards to accuracy.

Adaptive Online Learning Algorithms for Blind Separation: Maximum Entropy and Minimum Mutual Information

1997 ◽  
Vol 9 (7) ◽  
pp. 1457-1482 ◽  
Author(s):  
Howard Hua Yang ◽  
Shun-ichi Amari
Keyword(s):  
Mutual Information ◽  
Maximum Entropy ◽  
Learning Algorithm ◽  
Adaptive Method ◽  
Descent Method ◽  
Stochastic Gradient Descent ◽  
Gradient Descent Method ◽  
Natural Gradient ◽  
Blind Separation ◽  
Efficient Learning

There are two major approaches for blind separation: maximum entropy (ME) and minimum mutual information (MMI). Both can be implemented by the stochastic gradient descent method for obtaining the demixing matrix. The MI is the contrast function for blind separation; the entropy is not. To justify the ME, the relation between ME and MMI is first elucidated by calculating the first derivative of the entropy and proving that the mean subtraction is necessary in applying the ME and at the solution points determined by the MI, the ME will not update the demixing matrix in the directions of increasing the cross-talking. Second, the natural gradient instead of the ordinary gradient is introduced to obtain efficient algorithms, because the parameter space is a Riemannian space consisting of matrices. The mutual information is calculated by applying the Gram-Charlier expansion to approximate probability density functions of the outputs. Finally, we propose an efficient learning algorithm that incorporates with an adaptive method of estimating the unknown cumulants. It is shown by computer simulation that the convergence of the stochastic descent algorithms is improved by using the natural gradient and the adaptively estimated cumulants.

scholarly journals Adaptive Natural Gradient Method for Learning of Stochastic Neural Networks in Mini-Batch Mode

2019 ◽  
Vol 9 (21) ◽  
pp. 4568
Author(s):  
Hyeyoung Park ◽  
Kwanyong Lee
Keyword(s):  
Neural Networks ◽  
Gradient Descent ◽  
Learning Algorithm ◽  
Descent Method ◽  
Benchmark Problems ◽  
Stochastic Neural Networks ◽  
Gradient Descent Method ◽  
Natural Gradient ◽  
Convergence Properties ◽  
Data Set

Gradient descent method is an essential algorithm for learning of neural networks. Among diverse variations of gradient descent method that have been developed for accelerating learning speed, the natural gradient learning is based on the theory of information geometry on stochastic neuromanifold, and is known to have ideal convergence properties. Despite its theoretical advantages, the pure natural gradient has some limitations that prevent its practical usage. In order to get the explicit value of the natural gradient, it is required to know true probability distribution of input variables, and to calculate inverse of a matrix with the square size of the number of parameters. Though an adaptive estimation of the natural gradient has been proposed as a solution, it was originally developed for online learning mode, which is computationally inefficient for the learning of large data set. In this paper, we propose a novel adaptive natural gradient estimation for mini-batch learning mode, which is commonly adopted for big data analysis. For two representative stochastic neural network models, we present explicit rules of parameter updates and learning algorithm. Through experiments on three benchmark problems, we confirm that the proposed method has superior convergence properties to the conventional methods.

scholarly journals A Pre-Trained Fuzzy Reinforcement Learning Method for the Pursuing Satellite in a One-to-One Game in Space

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2253
Author(s):  
Xiao Wang ◽  
Peng Shi ◽  
Yushan Zhao ◽  
Yue Sun
Keyword(s):  
Reinforcement Learning ◽  
Gradient Descent ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Control Policy ◽  
Descent Method ◽  
Gradient Descent Method ◽  
One To One ◽  
Inference Systems ◽  
First Time

In order to help the pursuer find its advantaged control policy in a one-to-one game in space, this paper proposes an innovative pre-trained fuzzy reinforcement learning algorithm, which is conducted in the x, y, and z channels separately. Compared with the previous algorithms applied in ground games, this is the first time reinforcement learning has been introduced to help the pursuer in space optimize its control policy. The known part of the environment is utilized to help the pursuer pre-train its consequent set before learning. An actor-critic framework is built in each moving channel of the pursuer. The consequent set of the pursuer is updated through the gradient descent method in fuzzy inference systems. The numerical experimental results validate the effectiveness of the proposed algorithm in improving the game ability of the pursuer.

A Novel Interactively Recurrent Self-Evolving Fuzzy CMAC and Its Classification Applications

2015 ◽  
Vol 14 (03) ◽  
pp. 1550019
Author(s):  
Jyun-Guo Wang ◽  
Shen-Chuan Tai ◽  
Cheng-Jian Lin
Keyword(s):  
Structure Learning ◽  
Learning Algorithm ◽  
Descent Method ◽  
Superior Performance ◽  
Gradient Descent Method ◽  
Parameter Learning ◽  
Cerebellar Model Articulation Controller ◽  
Classification Problems ◽  
Fuzzy Cmac ◽  
Firing Strength

In this paper, an Interactively Recurrent Self-evolving Fuzzy Cerebellar Model Articulation Controller (IRSFCMAC) model is developed for solving classification problems. The proposed IRSFCMAC classifier consists of internal feedback and external loops, which are generated by the hypercube cell firing strength to itself and other hypercube cells. The learning process of the IRSFCMAC gets started with an empty hypercube base, and then all of hypercube cells are generated and learned online via structure and parameter learning, respectively. The structure learning algorithm is based on the degree measure to determine the number of hypercube cells. The parameter learning algorithm, based on the gradient descent method, adjusts the shapes of the membership functions and the corresponding fuzzy weights of the IRSFCMAC. Finally, the proposed IRSFCMAC model is tested by four benchmark classification problems. Experimental results show that the proposed IRSFCMAC model has superior performance than traditional FCMAC and other models.

A SEMI-PARAMETRIC HYBRID NEURAL MODEL FOR NONLINEAR BLIND SIGNAL SEPARATION

2000 ◽  
Vol 10 (02) ◽  
pp. 79-93 ◽  
Author(s):  
HANCHUAN PENG ◽  
ZHERU CHI ◽  
WANCHI SIU
Keyword(s):  
Learning Algorithm ◽  
Neural Model ◽  
Descent Method ◽  
Signal Separation ◽  
Hybrid Network ◽  
High Signal ◽  
Blind Signal Separation ◽  
Gradient Descent Method ◽  
Strong Nonlinearity ◽  
Blind Signal

Nonlinear blind signal separation is an important but rather difficult problem. Any general nonlinear independent component analysis algorithm for such a problem should specify which solution it tries to find. Several recent neural networks for separating the post nonlinear blind mixtures are limited to the diagonal nonlinearity, where there is no cross-channel nonlinearity. In this paper, a new semi-parametric hybrid neural network is proposed to separate the post nonlinearly mixed blind signals where cross-channel disturbance is included. This hybrid network consists of two cascading modules, which are a neural nonlinear module for approximating the post nonlinearity and a linear module for separating the predicted linear blind mixtures. The nonlinear module is a semi-parametric expansion made up of two sub-networks, one of which is a linear model and the other of which is a three-layer perceptron. These two sub-networks together produce a "weak" nonlinear operator and can approach relatively strong nonlinearity by tuning parameters. A batch learning algorithm based on the entropy maximization and the gradient descent method is deduced. This model is successfully applied to a blind signal separation problem with two sources. Our simulation results indicate that this hybrid model can effectively approach the cross-channel post nonlinearity and achieve a good visual quality as well as a high signal-to-noise ratio in some cases.

Meteorological Data Forecast using RNN

2017 ◽  
Vol 9 (1) ◽  
pp. 61-74 ◽  
Author(s):  
Stefan Balluff ◽  
Jörg Bendfeld ◽  
Stefan Krauter
Keyword(s):  
Neural Networks ◽  
Wind Speed ◽  
Linear Prediction ◽  
Learning Algorithm ◽  
Meteorological Data ◽  
System Modeling ◽  
Electrical Power ◽  
Descent Method ◽  
Gradient Descent Method ◽  
Earth System Modeling

Gathering knowledge not only of the current but also the upcoming wind speed is getting more and more important as the experience of operating and maintaining wind turbines is increasing. Not only with regards to operation and maintenance tasks such as gearbox and generator checks but moreover due to the fact that energy providers have to sell the right amount of their converted energy at the European energy markets, the knowledge of the wind and hence electrical power of the next day is of key importance. Selling more energy as has been offered is penalized as well as offering less energy as contractually promised. In addition to that the price per offered kWh decreases in case of a surplus of energy. Achieving a forecast there are various methods in computer science: fuzzy logic, linear prediction or neural networks. This paper presents current results of wind speed forecasts using recurrent neural networks (RNN) and the gradient descent method plus a backpropagation learning algorithm. Data used has been extracted from NASA's Modern Era-Retrospective analysis for Research and Applications (MERRA) which is calculated by a GEOS-5 Earth System Modeling and Data Assimilation system. The presented results show that wind speed data can be forecasted using historical data for training the RNN. Nevertheless, the current set up system lacks robustness and can be improved further with regards to accuracy.

Natural Gradient Learning Algorithms for RBF Networks

2015 ◽  
Vol 27 (2) ◽  
pp. 481-505 ◽  
Author(s):  
Junsheng Zhao ◽  
Haikun Wei ◽  
Chi Zhang ◽  
Weiling Li ◽  
Weili Guo ◽  
...  
Keyword(s):  
Fisher Information ◽  
Fisher Information Matrix ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Information Matrix ◽  
Descent Method ◽  
Gradient Descent Method ◽  
Natural Gradient ◽  
Rbf Networks ◽  
Gradient Learning

Radial basis function (RBF) networks are one of the most widely used models for function approximation and classification. There are many strange behaviors in the learning process of RBF networks, such as slow learning speed and the existence of the plateaus. The natural gradient learning method can overcome these disadvantages effectively. It can accelerate the dynamics of learning and avoid plateaus. In this letter, we assume that the probability density function (pdf) of the input and the activation function are gaussian. First, we introduce natural gradient learning to the RBF networks and give the explicit forms of the Fisher information matrix and its inverse. Second, since it is difficult to calculate the Fisher information matrix and its inverse when the numbers of the hidden units and the dimensions of the input are large, we introduce the adaptive method to the natural gradient learning algorithms. Finally, we give an explicit form of the adaptive natural gradient learning algorithm and compare it to the conventional gradient descent method. Simulations show that the proposed adaptive natural gradient method, which can avoid the plateaus effectively, has a good performance when RBF networks are used for nonlinear functions approximation.

scholarly journals Multiple instance learning tracking based on Fisher linear discriminant with incorporated priors

2018 ◽  
Vol 15 (1) ◽  
pp. 172988141775072 ◽  
Author(s):  
Zhiyu Zhou ◽  
Xu Gao ◽  
Jingsong Xia ◽  
Zefei Zhu ◽  
Donghe Yang ◽  
...  
Keyword(s):  
Motion Tracking ◽  
Learning Model ◽  
Descent Method ◽  
Multiple Instance Learning ◽  
Detection Methods ◽  
Gradient Descent Method ◽  
Tracking Accuracy ◽  
Fisher Linear Discriminant ◽  
Linear Discriminant ◽  
Tracking Object

Traditional tracking-by-detection methods use online classifier to track object, and the classifier can be degenerated easily using self-learning process. The article presents a multiple instance learning (MIL) tracking method based on a semi-supervised learning model with Fisher linear discriminant (MILFLD). First, the overlap rate of sampled instances and tracking object served as the prior information. Using both labeled and unlabeled data, the tracking drift problem in the learning model could be alleviated. Second, the lost function of MILFLD is built using Fisher linear discriminant model incorporated with priors. Hence the optimal classifier can be selected out directly in instance level. Last but not least, the classifiers are chosen by gradient descent method, assuring the maximum descent of lost function. Therefore, the classifiers selected at previous frames are still discriminative to future frames, which can help to constrain the error propagation. Comparison experiments show that the center location errors of online AdaBoosting , online MIL tracking, weighted MIL tracking (WMIL), compressive tracking (CT), struck tracking, and MILFLD are 78, 66, 62,74, 59, and 25 pixels, respectively, which demonstrates the tracking accuracy of our method. The experiments of robot motion tracking in realistic scenario have been complemented for comparison as well. Despite the variations in illumination, deformation, or occlusions of the objects, the proposed method can track the target accurately and has high real-time performance.

scholarly journals Building Recurrent Neural Networks to Implement Multiple Attractor Dynamics Using the Gradient Descent Method

2009 ◽  
Vol 2009 ◽  
pp. 1-11 ◽  
Author(s):  
Jun Namikawa ◽  
Jun Tani
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Recurrent Neural Network ◽  
Gradient Descent ◽  
Learning Algorithm ◽  
Transition Function ◽  
Descent Method ◽  
Van Der Pol Oscillator ◽  
Multiple Time ◽  
Gradient Descent Method

The present paper proposes a recurrent neural network model and learning algorithm that can acquire the ability to generate desired multiple sequences. The network model is a dynamical system in which the transition function is a contraction mapping, and the learning algorithm is based on the gradient descent method. We show a numerical simulation in which a recurrent neural network obtains a multiple periodic attractor consisting of five Lissajous curves, or a Van der Pol oscillator with twelve different parameters. The present analysis clarifies that the model contains many stable regions as attractors, and multiple time series can be embedded into these regions by using the present learning method.

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