scholarly journals Design of the Exercise Load Data Monitoring System for Exercise Training Based on the Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Panlong Qin ◽  
Wei Feng
Keyword(s):  
Neural Network ◽  
Exercise Training ◽  
Gradient Descent ◽  
Optimal Solution ◽  
Sports Injury ◽  
Parallel Optimization ◽  
Exercise Load ◽  
The Neural Network ◽  
Exercise Fatigue ◽  
Weight Correction

In order to monitor the sports load data of athletes in sports training, this paper studies the methods and systems of sports load monitoring and fatigue warning based on neural network technology. In this paper, the neural network parallel optimization algorithm based on big data is used to accurately estimate the motion load and intensity according to the determined motion mode and acceleration data, so as to realize the real-time monitoring of the exercise training. The results show that the value of η is usually small to ensure that the weight correction can truly follow the direction of the gradient descent. In this paper, 176 samples were extracted from the monitoring data collected by the “National Tennis Team Information Platform,” 160 of which were selected as training samples and the other 16 as test samples. Ant colony size M = 20. The minimum value Wmin of the weight interval is −2, and the maximum value Wmax is 2. The maximum number of iterations is set to 200. σ = 1; that is, only one optimal solution is retained. The domain is divided into 60 parts evenly; that is, r = 60. Generally, η can be taken as any number [28] between [10-3, 10], but the value is usually small to ensure that the weight correction can truly follow the direction of the gradient descent. In this paper, the value is 0.003. In the early warning stage of exercise fatigue, reasonable measurement units of exercise fatigue time were divided according to the characteristics of different exercise items. It is proved that the Bayesian classification algorithm can effectively avoid the sports injury caused by overtraining by warning the fatigue and preventing the sports injury caused by overtraining.

Research on Power Load Forecasting Model Based on Hybrid Algorithm Optimizing BP Neural Network

2014 ◽  
Vol 8 (1) ◽  
pp. 723-728 ◽  
Author(s):  
Chenhao Niu ◽  
Xiaomin Xu ◽  
Yan Lu ◽  
Mian Xing
Keyword(s):  
Neural Network ◽  
Optimization Algorithm ◽  
Load Forecasting ◽  
Optimal Solution ◽  
Electric Power System ◽  
Local Optimum ◽  
Good Prediction ◽  
Ant Colony Optimization Algorithm ◽  
The Neural Network ◽  
Power Load

Short time load forecasting is essential for daily planning and operation of electric power system. It is the important basis for economic dispatching, scheduling and safe operation. Neural network, which has strong nonlinear fitting capability, is widely used in the load forecasting and obtains good prediction effect in nonlinear chaotic time series forecasting. However, the neural network is easy to fall in local optimum, unable to find the global optimal solution. This paper will integrate the traditional optimization algorithm and propose the hybrid intelligent optimization algorithm based on particle swarm optimization algorithm and ant colony optimization algorithm (ACO-PSO) to improve the generalization of the neural network. In the empirical analysis, we select electricity consumption in a certain area for validation. Compared with the traditional BP neutral network and statistical methods, the experimental results demonstrate that the performance of the improved model with more precise results and stronger generalization ability is much better than the traditional methods.

A One-Layer Recurrent Neural Network with a Discontinuous Activation Function for Linear Programming

2008 ◽  
Vol 20 (5) ◽  
pp. 1366-1383 ◽  
Author(s):  
Qingshan Liu ◽  
Jun Wang
Keyword(s):  
Neural Network ◽  
Linear Programming ◽  
Recurrent Neural Network ◽  
Optimal Solution ◽  
Activation Function ◽  
High Gain ◽  
Discontinuous Activation ◽  
The Neural Network ◽  
Decision Variables ◽  
Discontinuous Activation Function

A one-layer recurrent neural network with a discontinuous activation function is proposed for linear programming. The number of neurons in the neural network is equal to that of decision variables in the linear programming problem. It is proven that the neural network with a sufficiently high gain is globally convergent to the optimal solution. Its application to linear assignment is discussed to demonstrate the utility of the neural network. Several simulation examples are given to show the effectiveness and characteristics of the neural network.

CHARACTERIZING ONE-LAYER ASSOCIATIVE NEURAL NETWORKS WITH OPTIMAL NOISE-REDUCTION ABILITY

1992 ◽  
Vol 06 (05) ◽  
pp. 1009-1025 ◽  
Author(s):  
TAO WANG ◽  
XIAOLIANG XING ◽  
XINHUA ZHUANG
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Cost Function ◽  
Noise Reduction ◽  
Gradient Descent ◽  
Storage Capacity ◽  
Learning Algorithm ◽  
Optimal Learning ◽  
The Neural Network ◽  
The Cost

In this paper, we describe an optimal learning algorithm for designing one-layer neural networks by means of global minimization. Taking the properties of a well-defined neural network into account, we derive a cost function to measure the goodness of the network quantitatively. The connection weights are determined by the gradient descent rule to minimize the cost function. The optimal learning algorithm is formed as either the unconstraint-based or the constraint-based minimization problem. It ensures the realization of each desired associative mapping with the best noise reduction ability in the sense of optimization. We also investigate the storage capacity of the neural network, the degree of noise reduction for a desired associative mapping, and the convergence of the learning algorithm in an analytic way. Finally, a large number of computer experimental results are presented.

GA-BP Neural Network Based Tire Noise Prediction

2012 ◽  
Vol 443-444 ◽  
pp. 65-70 ◽  
Author(s):  
Yong Che ◽  
Wang Xin Xiao ◽  
Li Jun Chen ◽  
Zhi Chu Huang
Keyword(s):  
Neural Network ◽  
Optimal Solution ◽  
Noise Prediction ◽  
Bp Network ◽  
Tire Noise ◽  
The Neural Network ◽  
Highly Nonlinear ◽  
Neural Network Structure ◽  
Trained Neural Network ◽  
Set Up

According to the complexity and the highly nonlinear characteristics of the tire sound, various parameters affecting tire noise were analyzed. By employing neural network a new method of tire noise prediction was proposed. Combining BP neural networks with genetic algorithms the noise prediction model was set up. In order to effectively predict tire noise, the neural network structure was designed and the input and output parameters of the network were determined. The genetic algorithm was added to the BP network in order to optimize initial weights and search out the optimal solution of the network. Applying laboratory drum method large amounts of tire noise test samples were obtained to train the BP network. Trained neural network can accurately predict tire noise in range of typical frequency bands. The results show that precision of this method is sufficient and the prediction effect is better.

scholarly journals Application Research on Fault Diagnosis of the Filter Unit Based on Intelligent Algorithm of GA and WNN

2015 ◽  
Vol 9 (1) ◽  
pp. 922-926 ◽  
Author(s):  
Zhao Xuejun ◽  
Wang Mingfang ◽  
Wang Jie ◽  
Tong Chuangming ◽  
Yuan Xiujiu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
High Speed ◽  
Analog Circuit ◽  
Optimal Solution ◽  
Intelligent Algorithm ◽  
The Neural Network ◽  
Filter Unit ◽  
Band Sequence ◽  
Neural Network Input

This paper focuses on the potential of GA algorithm for adaptive random global search, and WNN resolution as well as the ability of fault tolerance to build a multi intelligent algorithm based on the GA-WNN model using the filter unit of analog circuit for fault diagnosis. Construction of GA-WNN model was divided into two stages; in the first stage GA was used to optimize the initial weights, threshold, expansion factor and translation factor of WNN structure; while in the second stage, initially, based on WNN training and learning, global optimal solution was obtained. In the process of using analog output signal by using wavelet decomposition, the absolute value of coefficient of each frequency band sequence was obtained along with the energy characteristics of the cross joint, with a combination of feature vectors as the input of the neural network. Through the pretreatment method, in order to reduce the neural network input, neural grid size of neurons was reduced in each layer and the convergence speed of neural network was increased. The experimental results show that the method can diagnose single and multiple soft faults of the circuit, with high speed and high precision.

scholarly journals A Recurrent Neural Network for Nonlinear Fractional Programming

2012 ◽  
Vol 2012 ◽  
pp. 1-18 ◽  
Author(s):  
Quan-Ju Zhang ◽  
Xiao Qing Lu
Keyword(s):  
Neural Network ◽  
Fractional Programming ◽  
Initial Point ◽  
Optimal Solution ◽  
Feasible Region ◽  
Globally Convergent ◽  
The Neural Network ◽  
Interval Constraints ◽  
Simulation Results ◽  
Nonlinear Fractional Programming

This paper presents a novel recurrent time continuous neural network model which performs nonlinear fractional optimization subject to interval constraints on each of the optimization variables. The network is proved to be complete in the sense that the set of optima of the objective function to be minimized with interval constraints coincides with the set of equilibria of the neural network. It is also shown that the network is primal and globally convergent in the sense that its trajectory cannot escape from the feasible region and will converge to an exact optimal solution for any initial point being chosen in the feasible interval region. Simulation results are given to demonstrate further the global convergence and good performance of the proposing neural network for nonlinear fractional programming problems with interval constraints.

scholarly journals An efficient quantum multiverse optimization algorithm for solving optimization problems

2020 ◽  
Vol 9 (1) ◽  
pp. 27
Author(s):  
Samira Sarvari ◽  
Nor Fazlida Mohd. Sani ◽  
Zurina Mohd Hanapi ◽  
Mohd Taufik Abdullah
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection System ◽  
Quantum Interference ◽  
Optimization Problems ◽  
Detection System ◽  
Optimal Solution ◽  
The Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

<p>Due to the recent trend of technologies to use the network-based systems, detecting them from threats become a crucial issue. Detecting unknown or modified attacks is one of the recent challenges in the field of intrusion detection system (IDS). In this research, a new algorithm called quantum multiverse optimization (QMVO) is investigated and combined with an artificial neural network (ANN) to develop advanced detection approaches for an IDS. QMVO algorithm depends on adopting a quantum representation of the quantum interference and operators in the multiverse optimization to obtain the optimal solution. The QMVO algorithm determining the neural network weights based on the kernel function, which can improve the accuracy and then optimize the training part of the artificial neural network. It is demonstrated 99.98% accuracy with experimental results that the proposed QMVO is significantly improved optimization compared with multiverse optimizer (MVO) algorithms.</p>

scholarly journals GA-BP Neural Network Regression Model for Predicting the Soil Moisture of Ecological Slope Protection

2021 ◽  
Author(s):  
Dunwen Liu ◽  
Chao Liu ◽  
Yu Tang ◽  
Chun Gong
Keyword(s):  
Neural Network ◽  
Soil Moisture ◽  
Prediction Model ◽  
High Speed ◽  
Meteorological Factors ◽  
Meteorological Data ◽  
Prediction Method ◽  
Optimal Solution ◽  
Percentage Error ◽  
The Neural Network

Abstract The neural network optimized by genetic algorithm(GA) is an efficient and accurate prediction method, which can quickly find the optimal solution through high-speed computing capability and self-learning function. The neural network model optimized by GA is applied to the prediction of soil moisture of ecological slope protection, which provides reference for practical application of slope vegetation screening. In this paper, nine meteorological factors and soil moisture data were obtained by field monitoring instruments and related meteorological data. Considering the lag of meteorological factors, the neural network optimized by GA is used to predict the soil moisture of 8 meteorological data. The results show that the root mean square error (RMSE) and the mean absolute percentage error (MAPE) of the prediction model are 0.22726 and 0.41234%, respectively, indicating that the prediction model runs well. Through V-fold cross-validation, it is found that the prediction results of the model is accurate and stable. The algorithm combining artificial neural network and GA can well predict the soil moisture of ecological slope protection, with high prediction accuracy, and has a good application prospect in other fields.

scholarly journals The Power Cepstrum Calculation with Convolutional Neural Networks

2019 ◽  
Vol 19 (2) ◽  
pp. e13 ◽  
Author(s):  
Mario Alejandro García ◽  
Eduardo Atilio Destéfanis
Keyword(s):  
Neural Network ◽  
Fourier Transform ◽  
Gradient Descent ◽  
Descent Method ◽  
Intermediate Step ◽  
Gradient Descent Method ◽  
The Neural Network ◽  
Regression Problems ◽  
The One ◽  
Deep Learning Model

A model of neural network with convolutional layers that calculates the power cepstrum of the input signal is proposed. To achieve it, the network calculates the discrete-time short-term Fourier transform internally, obtaining the spectrogram of the signal as an intermediate step. The weights of the neural network can be calculated in a direct way or they can be obtained through training with the gradient descent method. The behaviour of the training is analysed. The model originally proposed cannot be trained in a complete way, but both the part that calculates the spectrogram and also a variant of the cepstrum equivalent to the autocovariance that keeps a big part of its usefulness can be trained. For the cases of successful training, an analysis of the obtained weights is done. The main conclusions indicate, on the one hand, that it is possible to calculate the power cepstrum with a neural network; on the other hand, that it is possible to use these networks as the initial layers of a deep learning model for the case of trainable models. In these layers, weights are initialised with the discrete Fourier transform (DFT) coefficients and they are trained to adapt to specific classification or regression problems.

Neural networks for prediction of robot failures

2013 ◽  
Vol 228 (8) ◽  
pp. 1444-1458 ◽  
Author(s):  
Ali Diryag ◽  
Marko Mitić ◽  
Zoran Miljković
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Optimal Solution ◽  
Feedforward Neural Networks ◽  
Real Data ◽  
Obstacle Detection ◽  
Network Training ◽  
Novel Approach ◽  
The Neural Network ◽  
Prediction Approach

It is known that the supervision and learning of robotic executions is not a trivial problem. Nowadays, robots must be able to tolerate and predict internal failures in order to successfully continue performing their tasks. This study presents a novel approach for prediction of robot execution failures based on neural networks. Real data consisting of robot forces and torques recorded immediately after the system failure are used for the neural network training. The multilayer feedforward neural networks are employed in order to find optimal solution for the failure prediction problem. In total, 7 learning algorithms and 24 neural architectures are implemented in two environments – Matlab and specially designed software titled BPnet. The results show that the neural networks can successfully be applied for the problem in hand with prediction rate of 95.4545%, despite having the erroneous or otherwise incomplete sensor measurements invoked in the dataset. Additionally, the real-world experiments are conducted on a mobile robot for obstacle detection and trajectory tracking problems in order to prove the robustness of the proposed prediction approach. In over 96% for the detection problem and 99% for the tracking experiments, neural network successfully predicted the failed information, which evidences the usefulness and the applicability of the developed intelligent method.

Sign in / Sign up

Export Citation Format

Share Document