Study on the Application of Data Mining-Based BP Neural Network
Forecasting Model in Physical Training
Ye Chen
Department of Physical Education, Hunan Normal University, [email protected]
Abstract
The data mining technology can be used to extract potentially useful information and knowledge from a large amount of incomplete, noisy, fuzzy and random data in practical application. Based on the feed-forward calculation, weight adjustment and learning algorithm of the data mining technology and BP neural network, this paper uses the BP neural network to establish a comprehensive sports performance forecasting model of the athletes, and the athlete physical training data of the high jump team in a physical culture institute in Hubei Province during 2009-2011 were combined to verify the accuracy of the forecasting model and analyze the causes of errors. It has a high degree of approximation in forecasting the sports performance, which can provide the coach with the basis to grasp the training status of the athletes.
Keywords
:Data Mining, Neural Network, Physical Training
1. Introduction
Since the adoption of the reform and opening-up policy, China’s sports achievements have attracted worldwide attention, and in particular, with the increasing application of the database technology, a massive amount of sports training data have been accumulated in the field of sports [1]. How to effectively use these data to find the pattern and serve the decision-makers has become one of the focuses in domestic and foreign researches. With the development of competitive sports in China, there is also a trend of scientification in physical training, and the difficulty of decision-making and forecasting in physical training has also increased. The purpose of data mining is to rapidly and effectively extract required answers from the data in the sports statistic reports. The back propagation algorithm of artificial neural network has strong function approximation capacity. The forecasting model established based on the BP neural network can be used to forecast the comprehensive sports performance of the athletes and provide assistance in physical training [2].
2. Data Mining Technology
2.1. Generation of the Data Mining Technology
With the rapid development of the information technology, the database size has continued expanding, and the data volume has also increased. The large amount of data tends to confuse people’s judgment, and the traditional query method cannot satisfy the requirement of data mining. In order to better use the data, extract useful information behind the data and provide help to the decision makers, new data analysis technology has been developed.
In the 1989 International Joint Conference on Artificial Intelligence, the concept of KDD (Knowledge Discovery in Databases) was proposed, which represents the knowledge discovered in data. Later, a new information technology—data mining—emerged [3]. Generation of the data mining technology represents the evolution process of the database technology, and it is not only the result of long-term study and development of the database technology, but also the result of the natural evolution of the information technology. At present, the data mining technology is an emerging interdisciplinary field, which involves knowledge on various aspects, such as the data warehouse technology, artificial intelligence, genetic algorithm, neural network, advanced mathematics, statistics and data visualization [4].
2.2. The Concept of Data Mining Technology
The data mining technology refers to the process to extract hidden, unknown and potentially useful information and knowledge from a large amount of incomplete, noisy, fuzzy and random databases, which is an interdisciplinary field of application. Through analysis of each data, a pattern can be found from a large amount of data. The tasks of data mining include correlation analysis, cluster analysis, classification analysis, exception analysis, special group analysis and evolution analysis [5].
This definition consists of the following four aspects: (1) The data source must be accurate, massive and noisy;
(2) The discovered knowledge must have certain attraction to the user;
(3) The discovered knowledge must be acceptable and understandable, which should also be easy to express and use;
(4) It is not required to discover the universal knowledge, and the discovered knowledge can only support certain precondition and constraint condition, and apply to a certain field.
Data mining consists of the following three steps:
(1) Data preparation: select required data from related data source (data warehouse or data mining market), and integrate them into datasets used in data mining through data cleansing and transformation;
(2) Pattern seeking: use certain methods (database technology, artificial intelligence technology, decision tree, statistic analysis, etc.) to find the pattern contained in the datasets;
(3) Pattern expression: express the pattern in a way that the user can understand (such as visualization), and provide decision support.
2.3. Process of Data Mining
Data mining mainly consists of the following several steps: First of all, the purpose of data mining should be recognized;
Secondly, the require data should be obtained, and initial data integration and inspection should be conducted;
Thirdly, data preprocessing should be conducted, including data cleansing, data integration, data transformation and data reduction;
Fourthly, methods such as the decision tree, classification, cluster, rough set, association rules and sequential pattern should be used to select corresponding algorithms and appropriate parameters and to analyze the data;
Fifthly, the results of data mining should be tested and verified, which usually needs to use the visualization technology;
Finally, the knowledge should be interpreted, and the knowledge obtained through analysis should be used into the organizational structure of the operational information system.
Because data mining includes massive preparation and planning work, 80% of the time and energy have to be spent in the data preparation phase. Therefore, before analysis of the data mining technology, adequate preparation work should be done [6].
Figure 1 shows the classic structure chart of the data mining system. In accordance with Figure 1, a classic data mining system is based on the database, data warehouse, the world-wide-web and other information repository, it enters the database or data warehouse server through data cleansing, integration and selection, then, it completes the data mining work in the data mining engine, knowledge base and pattern evaluation, and it finally feeds back it to the user through the user interface.
Figure 1. Classic Structure Chart of the Data Mining System
3. Application of the Data Mining Technology in China’s Sports
With the wide application of databases and the network technology, the data mining technology has been widely applied in various industries, such as marketing, finance and insurance, communications network and product manufacturing.
In 2008, Beijing successfully held the Olympic Games, in which, the Chinese athletes won 51 gold medals, and for the first time, China ranked No.1 in the summer Olympic gold medal table. In this Olympic Games, the Chinese athletes won 100 medals in total, which shows that China’s sports have reached the advanced international level. The data mining technology has been widely used in China’s sports research field. By looking up into China Journal Full-text Database, China Academic Journal Network Publishing Database, China Important Conference Paper Full-text Database and China Excellent Master and Doctoral Dissertation Full-text Database, we find that since 2002, there have been more than 40 papers of the application of data mining in the scientific researches of sports. Applications of the data mining technology in sports include: physical fitness test data, information of the competitor, sports training monitoring, game strategies and sports information management.
In his paper “Study on the Application of Data Mining Technology in Technique and Tactics Analysis of Pingpong Match”, by obtaining the technique and tactics parameters of single motion of excellent Chinese athletes in every round of important international games, Gao Hongge introduced the mining results of the FP-growth algorithm in the technique and tactics analysis of Pingpong match [7]. In addition, he also introduced the K-Means dynamic clustering algorithm and its application/realization process and mining results, and developed the software of “Technique and Tactics Statistic Analysis System of Pingpong Match”.
In "Study and Realization of the Agent-Based Decision Support System of Physical Training Management" of Mei Cheng (2008), based on introduction of the decision support system, agent and related theory and technology of MAS, the data mining technology and association rule mining technology were studied, and the improved M-Apriori algorithm with higher execution efficiency and compatibility with the information of the students’ physical fitness was obtained, which was applied in the decision support system [8]. In accordance with the characteristics of physical training management, the multi-agent system was designed for the physical training management, and the physical training management DSS based on multi-agent was obtained.
In “Study and Realization of the Decision Support System Based on Data Mining” of Chi Dianwei (2008), based on specific analysis of the basic theory, method, technology and data mining algorithm of association rule, appropriate improvement was made to the Apriori algorithm, and the index
user interface
evaluation pattern
data mining engine
data cleansing, integration and selection data warehouse server world-wide-web
other information repository knowledge base
the physical performance and physical examination reports of the students of Nanchang University, and through application of the established model for the improved data mining algorithm, corresponding training plan can be made in accordance with the rules in the knowledge base and the physical situation of the students, which can provide effective decision support during the college students’ physical training.
In his “Application of the Data Mining Technology in Modern Sports Researches”, based on the questionnaires of the speed-type athletes of a certain province, Zhu Weidong [9] (2010) collected 12,528 data of 343 master-level , level-one and level-two athletes, and after processing the database, the information and data sheet of this group was obtained; then, the data mining software and the ID3 algorithm were used to obtain the decision tree of the basic information; finally, the Apriori algorithm was used to obtain the association rules of the basic information and data of the excellent athletes in this group, and characteristics such as the athletes were generally older, had long trainings and did not have significant gender difference were obtained.
In “Mining and Analysis of China’s Sports Statistical Data”, in accordance with the data and information in the 2001-2009 “China’s Sports Statistical Yearbook”, Wang Fan [10] (2011) adopted various methods such as document literature, mathematical statistics and data mining, and used grey forecasting analysis, grey correlation analysis, cluster analysis and factor analysis to establish the forecasting model of the “number of world champions”, discuss the impact of various factors on the “number of world champions”, such as “expenditure of excellent sports team”, “expenditure of sports infrastructure”, “national excellent athletes”, “total number of coaches” and “referees of various levels”, and also to conduct the cluster and level sequencing of the sports development of various regions in China, which has shown that: the data mining technology is very helpful in discovering potential association in massive data and in deeper analysis of the data and information; the time sequence development trend of human resource input is closer to the reference sequence of the “number of world champions” than that of the expenditure input.
4. Application of Neural Network in Physical Training
4.1. Introduction of Neural Network
Artificial neural network (ANN for short) or neural network (NN for short) is a complicated network system formed by a large amount of mutually connected simple neurons, which has strong adaptability, nonlinear mapping capability, robustness and fault-tolerant capability. Researches on artificial neural network started in the 1940s. In 1943, the psychologist W. McCulloch worked with the mathematical logician W. Pitts to propose the famous MP Model. Later, the American biophysicist Hopfield proposed the Hopfield Model in 1982, which provided an explicit method to determine the balance and stable state of the artificial neural network. Artificial neural network reflects many basic features of the human brain functions, which is a highly complicated nonlinear system. It was proposed based on modern neurobiology and cognitive science’s studies on information processing by the human, which tried to simulate the way in which the human neural system processes and memorizes the information to design a kind of signal processing system with the characteristics of human brain.
The back propagation algorithm (BP for short) is a basic method to train the artificial neural network, which was developed and designed by D. E. Rumelhart, J. K. McCelland and their research team in 1986. It can realize arbitrary nonlinear mapping between the input and output, and it has strong function approximation capacity. Among the three types of neural networks (BP, RBF and CMAC), the neural network based on the BP algorithm has the widest application [11].
The BP network can learn and store a large amount of input—output mode mapping relations without pre-disclosure of the mathematical equation that describes this mapping relation. Its learning rule is to use the steepest descent method to continuously adjust the weight and threshold values of the network through back propagation, so that minimum sum of square errors can be realized. The topological structure of the BP network model includes the input layer, hidden layer and output layer, as shown in Figure 2.
Figure 1. BP Neural Network
4.2. Feed-forward Calculation of BP Neural Network
During the learning phase of this network, there are N input nodes, L nodes in the hidden layer and M nodes in the output layer, then, the output of the Jth node in the hidden layer is:
1 M j ij i i
net
O
(1) The output of the jth node is:
j j
O
f net
(2) In which, f (netj) is the excitation function:
01
1
j j j netf net
e
(3) Where:θj——threshold value, whether it is positive or negative represents the position of the excitation
function through left-right movement along the horizontal axis;
θ0——It determines the shape of the δ function, when θ0 is small, the δ function is closer to a step
function, and when θ0 is big, the δ function is closer to be flat.
In accordance with the above formula, we obtain:
'
1
j j j
f net
f net
f net
(4) The output of the jth node— O
j will be transmitted forward to the kth node through the weighting
coefficient ωik, and the total input of the kth node in the output layer is: 1 q k jk j j
net
O
(5)The actual network output of the kth node in the output layer is:
k kO
f net
(6) Input Layer Hidden Layer Output Layerduring the propagation process so that the output result will be as close to the target output value dk as
possible. After modification of the network connection weight of this sample P has been completed, another group of training sample should be delivered to conduct similar learning.
4.3. Weight Adjustment of BP Neural Network
Assume the quadratic error function of the input-output mode pair of each sample is defined as:
2 11
2
t p pk pk kE
d
O
(7)Then, the total system error is:
2 1 1 11
2
P L P pk pk p p k pE
d
O
E
(8)Where, P refers to the sample mode logarithm, and L refers to the number of network output nodes. The weight coefficient should be adjusted in the opposite direction of the gradient change of E function, so the network output will be close to the expected output.
The modified formula of the weight coefficient is:
jk jk
E
(9)Where, η refers to the learning efficiency and η＞0.
k jk k jk
net
E
E
net
(10)The back propagation error signal δk is defined as:
k k k k k
O
E
E
net
O
net
(11)(
k k)
kE
d
O
O
(12) '(
)
(
)
k k k k kO
f net
f net
net
net
(13)We can deduce that:
'
(
) (
)
(1
)(
)
kd
kO f net
k kO
kO
kd
kO
k
(14) Because 1(
)
q k jk j j j jk iknet
O
O
(15)Therefore, we can obtain the modified formula of arbitrary neuron weight coefficient in the output layer as: '
(
) (
)
jkd
kO
kf net O
k j kO
j
(16) i.e.,(1
)(
)
jkO
kO
kd
kO O
k j
(17) The variation of the computation weight coefficient is:
' ij ij j j ij j i j j iE
net
E
net
E
f
net
O
net
O
(18) 1 1 1 1 1 L k k j k j q L jk j k k j j L jk k k L k jk knet
E
E
O
net
O
E
O
net
O
E
net
(19) Because
' 1 L j j k jk kf
net
(20)Plug the sample sign P into the formula, and we can obtain: For the output node k:
'
(
)
(1
)(
)
p
jk
f
net
pkd
pkO
pkO
pj
O
pkd
pkO
pkO
pj
(21) For the hidden node j:
' 1 1(1
)
L L p ij pj pk jk pi pj pj pk jk pi k kf
net
O
O
O
O
(22)Where, Opk refers to the output of the output node k, Opj refers to the output of the hidden node J,
and Opi refers to the output of the input node i.
Where, α is the smoothing factor, and 0<α<1.
4.3. Computation Steps of the Learning Algorithm of BP Neural Network
(1) Set all weights ωij, ωij and the threshold value θj as random number between [-1, 1]; assign the
input vector X= (x1, x2, …, xn) used in the learning and training and the expected output vector D= (d0,
dl, …, dl); the neuron output in the middle layer is Oj=f (netj), and that in the end output layer is Ok=f
(netk).
(2) The error signal between the expected output and actual output of the network is transmitted from the output layer through the middle layer and then to the input layer, which is a layer-to-layer process to modify the connected “back propagation of error”: calculate the difference between the actual output and the target value:
2 11
2
t p pk pk kE
d
O
(24)Calculate the weight adjustment amount:
'(
)
(1
)(
)
p
jk
f
net
pkd
pkO
pkO
pj
O
pkd
pkO
pkO
pj
(25)
' 1 1(1
)
L L p ij pj pk jk pi pj pj pk jk pi k kf
net
O
O
O
O
(26)(3) The network “memory training” process with alternate “forward propagation of mode” and “back propagation of error”,
1
k k
(27)(4) The “learning and convergence” process in which the global error of the network moves towards the minimal value: calculate the error between the target value and the output value at this moment once again, and when the error satisfies the requirement, the learning process is over; otherwise, return to step (2) to continue iteration [12].
4.4. Use BP Neural Network to Establish the Forecasting Model
Because the input layer of the BP neural network serves as the buffer storage, therefore, the data can be loaded to the forecasting model. The node number in the input layer of the model equals the dimension number of the data source. After calculation in the model, the calculation results of the forecasting model are output through the output layer. The athlete physical training data of the high jump team in a physical culture institute in Hubei Province during 2009-2011 were selected, and in accordance with the data of 20 physical training programs, the comprehensive sports performance of the athletes could be forecasted.
Because there were 20 physical training programs, the dimension number of the input layer in the forecasting model of BP neural network was set as 20, the dimension number of the output layer was set as 1, and the forecasted comprehensive sports performances of the athletes were output. The error signal between the expected output and actual output of the network was transmitted from the output layer through the middle layer and then to the input layer, which was a layer-to-layer process to modify the connected “back propagation of error”. Increased hidden layers can increase the processing capability of the model, but during the convergence process, the more neurons there are in the hidden layer, the more times of iterative computation are needed, which would further increase the complexity and times of training. During this forecasting, the neuron number in the hidden layer was set as 8.
4.5. Analysis of the Forecasting Result
During the experiment, the sample data were divided into two groups of training data and test data. The training data were used to train the BP neural network in order to determine the weight, while the test data were used to test the pre-trained BP neural network. Of which, the training data accounted for 90% of the total, while the test data accounted for 20%.
The outputs of the training data and test data obtained after 2500 trainings of the BP neural network model are shown in Table 1 and Table 2 respectively.
Table 1 Output of Training Data
Target Value Output Value Error (%)
Lowest Score (m) 1.9675 1.9677 0.0102
Highest Score (m) 2.0467 2.0468 0.0049
Average Score (m) 1.9865 1.9863 -0.0101
Mean Square Error 1.2153 1.2149 -0.0329
Table 2 Output of Test Data
Target Value Output Value Error (%)
Lowest Score (m) 1.9687 1.9689 0.0102
Highest Score (m) 2.0459 2.0461 0.0098
Average Score (m) 1.9834 1.9837 0.0151
Mean Square Error 1.2147 1.2150 0.0247
Through comparison of Table 1 and Table 2, we can see that: the BP neural network model has great learning capability and adaptability, which has high degree of approximation in forecasting the sports performance, and it should be promoted.
However, we also noticed that there was certain distortion in the forecasting results obtained by the BP neural network model, and the reasons mainly include: (1) The data sample had a limited size; (2) There was error during the testing and recording process of the physical training data of the athletes. Therefore, when BP neural network is used to conduct data mining, the noise data can be further eliminated to optimize the sample.
5. Conclusion
(1) With the flourish of China’s sports, a large amount of physical training data have been accumulated in the field of sports, and how to process these data and find their pattern is an urgent problem.
(2) Through analysis of each data, the data mining technology can find a certain pattern in massive data and discover the useful information hidden behind the data, in this way to provide help to the decision makers. The data mining technology has been widely applied in the field of sports.
(3) Artificial neural network (ANN) has strong adaptability, nonlinear mapping capability, robustness and fault-tolerant capability, and the back propagation algorithm has strong function approximation capacity.
(4) The forecasting model established based on the BP neural network can be used to forecast the comprehensive sports performance of the athletes. Through corresponding forecasting technology and mining of the physical training data of the athletes of the high jump team in a physical culture institute in Hubei Province during 2009-2011, we find that this model has great learning capability and adaptability as well as a high degree of approximation in forecasting the sports performance, which can provide the coach with the basis to grasp the training status of the athletes.
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