Journal of Applied Mathematics

Volume 2013 (2013), Article ID 983051, 9 pages

http://dx.doi.org/10.1155/2013/983051

Research Article

Incomplete Phase Space Reconstruction Method Based on Subspace Adaptive Evolution Approximation

Tai-fu Li,¹ Wei Jia,² Wei Zhou,¹ Ji-ke Ge,¹ Yu-cheng Liu,¹ and Li-zhong Yao¹

¹School of Electrical and Information Engineering, Chongqing University of Science and Technology, Chongqing 401331, China

²School of Electronic Engineering, Xi'an Shiyou University, Xi’an 710065, China

Received 19 July 2013; Accepted 19 September 2013

Academic Editor: Baocang Ding

Copyright © 2013 Tai-fu Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

The chaotic time series can be expanded to the multidimensional space by phase space reconstruction, in order to reconstruct the dynamic characteristics of the original system. It is difficult to obtain complete phase space for chaotic time series, as a result of the inconsistency of phase space reconstruction. This paper presents an idea of subspace approximation. The chaotic time series prediction based on the phase space reconstruction can be considered as the subspace approximation problem in different neighborhood at different time. The common static neural network approximation is suitable for a trained neighborhood, but it cannot ensure its generalization performance in other untrained neighborhood. The subspace approximation of neural network based on the nonlinear extended Kalman filtering (EKF) is a dynamic evolution approximation from one neighborhood to another. Therefore, in view of incomplete phase space, due to the chaos phase space reconstruction, we put forward subspace adaptive evolution approximation method based on nonlinear Kalman filtering. This method is verified by multiple sets of wind speed prediction experiments in Wulong city, and the results demonstrate that it possesses higher chaotic prediction accuracy.

1. Introduction

In recent years, industrial disasters and accidents occurred frequently, the meteorological and hydrological conditions were complicated and changeable, and financial markets fluctuated drastically. These phenomena often contain chaotic characteristics [1, 2], and prediction [3] for these phenomena is imminent. For a long time, there was no scientific tool for handling this issue, because the changing mechanisms of characteristics in these phenomena were not understood very well. Hence, aiming at the chaotic characteristics, some scholars worked with structures and made a lot of new researches on the prediction of chaotic time series [4–8].

To study and deal with the measurement data of chaotic system, Kennel et al. presented the reconstruction method of phase space system. Two parameters, the embedding dimension and delay time , needed to be determined before the phase space reconstruction [9, 10]. At present, time delay selection methods that are commonly used in the chaotic short-term prediction mainly include autocorrelation method [11], mutual information method [12], and singular value fraction method [13]. Calculating methods of embedding dimension mainly include saturated correlation dimension [14], false nearest neighbors method [15], and Cao's method [16]. Hu and Chen put forward the C-C method [17], which can simultaneously estimate the delay time and embedding dimension with the correlation integral. Autocorrelation method extracts only linear correlation degree between time series, which is hard to be applied to high-dimensional chaos system and nonlinear dynamical system. Mutual information method, which can determine the optimal delay time by calculating the first minimal value of mutual information function, is a nonlinear analysis method, but it cannot avoid massive calculation and cannot satisfy the requirement of complicated space division. It is difficult to determine the threshold of the singular value, because the singular value fraction method is largely affected by noise. When the embedded dimension with the saturated correlation dimension is calculated, the main question is to choose the different neighborhood radius. The radius selection has certain randomness, and the result will be in large deviation with improper choice, because of the influence of noise in the data and excessive concentration of the data. The determination of threshold has very strong subjectivity when we use false nearest neighbors method to determine the embedding dimension. There is no objective standard to determine the threshold value, especially for the experimental data, which may get a wrong result. Cao's method, an improved false nearest neighbors method, can effectively distinguish random signals and deterministic signals, and embedding dimension can be obtained through a less amount of data. C-C method is based on the statistical theory, so cannot be precisely determined.

Researches have showed that different phase space reconstruction methods get different and . Moreover, the same chaotic time series with the same kind of method in different times may get different and . There is no phase space reconstruction that can obtain complete and independent phase space. After phase space reconstruction, prediction model is often established through the functional approximation method.

The prediction model based on phase space reconstruction has been used to adopt the functional approximation method based on the neural network [18–21], which has strong nonlinear fitting capability and can approximate any complex nonlinear relationships. However, since neural network is only suitable for approximation of a deterministic system, it is difficult to guarantee the time-varying system performance and ensure its generalization performance in other untrained neighborhood. Meanwhile, the prediction effect of neural network is not good, because the chaotic time series is a complex nonlinear uncertain system.

In this study, we introduce Kalman filtering to neural network model [22], inspired by Kalman iteration and Bucy and Sunahara’s nonlinear extended Kalman filtering theory [23]. The subspace approximation of neural network based on the nonlinear extended Kalman filtering (EKF) has a function which is dynamic evolution approximation from one neighborhood to another. Therefore, we can constitute a phase space by choosing a kind of phase space reconstruction method, and the space may be incomplete, not separate, and can be seen as a subspace of the ideal phase space. On this basis, we put forward adaptive neural network model based on nonlinear Kalman filtering and finally realize the subspace approximation of dynamic evolution system. In addition, we simulate wind speed series in Wulong city using the proposed method. By comparing with BP neural network prediction model, the results show that our method possesses higher prediction accuracy.

The paper is organized as follows. Section 2 discusses about the subspace approximation of phase space reconstruction. In Section 3, we describe the neural network model based on nonlinear Kalman filtering. Section 4 uses practical examples and series tests to verify the proposed method, while Section 5 contains the conclusions of the present work.

2. Subspace Approximation of Phase Space Reconstruction

Reconstructing phase space by chaos theory needs to identify the chaos of time series. Single variable time series can be reconstructed into a phase space by Takens’ embedding theorem in phase space reconstruction [24, 25]; that is, the original dynamical system can be restored in the sense of topological equivalence as long as the embedding dimension is sufficiently high. For the observed time series , after time delay reconstruction by Takens embedding theorem, it will receive a set of space vector

After phase space reconstruction, the data space is

Accordingly, we acquire

where is a single-valued function. Then, we have

However, it cannot be really obtained as the data are often limited. Hence, can only be constituted by limited measurement data, making sufficiently approximate to , consequently we can get a nonlinear prediction model.

This paper employs the neural network to predict chaotic series. However, the neural network cannot readily handle the inconsistency of the phase space reconstruction because of uncertain nonlinear chaotic time series. Therefore, it is crucial to adaptively construct subspace to approximate chaotic series through the incomplete phase space. The feature of adaptive subspace approximation is that it can add new data in real time and forget old data in the process of training. Consequently, weights and thresholds of the neural network are continuously modified to realize the dynamic evolution modeling.

3. Neural Network Model Based on Nonlinear Kalman Filtering

Kalman filtering has good adaptability. It can dynamically update and forecast the system information in real time with limited data. However, it cannot be readily used for complicated nonlinear model. Meanwhile, the extended Kalman filtering (EKF) is a kind of effective method to handle nonlinear filtering.

The mathematical model of EKF is as follows:

where and are independent, zero mean, and Gaussian random processes with covariance matrices and , respectively. The statistical properties are as follows:

EKF spreads nonlinear functions and to Taylor series around filtering value and predicted value , respectively, only retaining the first-order information. Hence, the linearization model of the nonlinear system is obtained, and then we can obtain the EKF formula in nonlinear system by basic equations of Kalman filtering.

Given a forward network with layers, the numbers of neurons in each layer are   . Suppose that input layer is the first layer and output layer is the layer. The weights of the layer neurons are   . In order to convert the calculation of connection weights in the above problem into filter recursive estimation form, we let all of the network weights constitute the state vector

where state vector consists of all of the weights according to the linear array, and its dimension is as follows:

Then the state equation and measurement equation of the system can be expressed as

where is the expected output, is the input vector, and is the actual output.

The measurement noise is assumed to be additive, white, and Gaussian, with zero mean and with covariance matrix defined by

Suppose that the output of the node for the layer in the iteration is

From (10) and (12), we have

Assume that

Accordingly, the measurement equation may also be expressed as

The Jacobian matrix of the function is described by

Similarly, all thresholds of the network constitute the state vector

where the dimension is

Suppose that and are both state variable; that is, the state vector composed of weights and thresholds is described by

Kalman filtering algorithm on training weights and thresholds of the neural network is as in Table 1.

Table 1: Extended Kalman filtering neural network algorithm.

Extended Kalman filtering neural network

(1) Initialization

(2) Time update (forecast)

(3) Measurement update (correct)

and are process noise covariance and measurement noise covariance, respectively, is the Jacobian matrix of observable model, is the optimal predictive value for step according to step , and is the optimal filter estimate for step .

4. Simulation Examples

4.1. Determining of Embedding Dimension and Delay Time

One of the most popular chaos logistic mapper is selected as the study object. Logistic equation is

The related time series are produced according to (20). It is a chaotic system when . Assume that initial value of series is 0.1, and 4000 points are calculated. The first 1000 points are eliminated as transition phenomenon, leaving the remaining 3000 points to reconstruct phase space. Before the phase space reconstruction, we determine the embedding dimension and delay time . A comparison among several methods is present in Table 2.

Table 2: Comparison among phase space reconstruction methods.

Parameter
Method

Autocorrelation
Mutual information
False nearest neighbors
Cao
C-C

1
12
—
—
5

—
—
4
3
5

“—” means nothing.

Obviously, the optimal embedding dimension and delay time are generally different by different methods of phase space reconstruction.

In order to verify the fact that data at different time will obtain different embedding dimension and delay time with the same phase space reconstruction method, we have the following experiment.

The remaining 3000 points () are divided into five parts, with time intervals , , , , and , respectively. Embedding dimension and delay time are present in Table 3 by C-C method.

Table 3: Parameters of the same phase space reconstruction during different time periods.

Parameter
Interval

5
3
4
4
5

5
5
4
9
10

Apparently, the data during different time periods will acquire different embedding dimension and delay time by using the same phase space reconstruction method.

4.2. Wind Speed Chaotic Series Forecasting Simulation

Analysis about the chaotic characteristics of wind speed in the process of wind power generation has been presented in a related article [26]. We record one of the wind speed data every 10 minutes, and 150 groups of wind speed data in Wulong city are used to simulate experiments in our study. We obtain the corresponding and by different phase space reconstruction methods, as shown in Table 4.

Table 4: Comparison among phase space reconstruction methods.

Parameter
Method

Autocorrelation
Mutual information
False nearest neighbors
Cao
C-C

1
12
—
—
3

m
—
—
3
7
4

Various combinations are present in Table 5.

Table 5: Various combinations on two forecasting methods.

Model
Combination
Parameter
Forecasting

a1
Autocorrelation + false nearest neighbors
,
BPNN

b1
Autocorrelation + false nearest neighbors
,
EKFNN

a2
Mutual information + false nearest neighbors
,
BPNN

b2
Mutual information + false nearest neighbors
,
EKFNN

a3
Autocorrelation + Cao
,
BPNN

b3
Autocorrelation + Cao
,
EKFNN

a4
Mutual information + Cao
,
BPNN

b4
Mutual information + Cao
,
EKFNN

a5
C-C
,
BPNN

b5
C-C
,
EKFNN

Wind speed prediction [27, 28] of chaotic time series about neural network model usually extracts phase space reference points as the BP neural network training samples on the basis of phase space reconstruction. We establish the neural network model based on nonlinear Kalman filtering, including two parts: predict wind speed and constantly modify weights and thresholds of the neural network by Kalman recursion. In this paper, BPNN model in the same structure is employed to forecast wind speed time series, in order to illustrate the validity of EKFNN on predicting the chaotic time series. The same 150 groups of wind speed data are used to simulate experiments. The predicted curves and error curves are shown in Figures 1, 2, 3, 4, and 5.

(a)   The predicted wind speed data

(b) Relative wind speed error

Figure 1: The effect comparison between a1 and b1.

(a)   The predicted wind speed data

(b) Relative wind speed error

Figure 2: The effect comparison between a2 and b2.

(a)   The predicted wind speed data

(b) Relative wind speed error

Figure 3: The effect comparison between a3 and b3.

(a)   The predicted wind speed data

(b) Relative wind speed error

Figure 4: The effect comparison between a4 and b4.

(a)   The predicted wind speed data

(b) Relative wind speed error

Figure 5: The effect comparison between a5 and b5.

Comparisons among several models in four indices are present in Table 6.

Table 6: Different wind speed model index.

Model
Error

MAE
MRE
MSE
SSE

a1
0.8482
0.1076
1.3786
206.7967

b1
0.3365
0.0416
0.1919
28.7834

a2
1.2279
0.1674
2.9614
444.2152

b2
0.6190
0.0759
0.6209
93.1357

a3
0.5106
0.0656
0.4089
61.3361

b3
0.3783
0.0458
0.2654
39.8130

a4
2.4005
0.3079
8.4766

b4
0.0852
0.0110
0.0177
2.6584

a5
0.8952
0.1173
1.7970
269.5495

b5
0.2867
0.0357
0.1359
20.3850

MAE, MRE, MSE, and SSE are Mean Absolute Error, Mean Relative Error, Mean Square Error, and Sum of Squared Error, respectively.

We list 12 groups, a total of 2 hours of wind speed forecasting results in two methods, under the same phase space reconstruction. Compare the prediction performance in the next 10 min, 20 min, 30 min, and up to, 120 min.

Comparisons among several prediction results in two methods are present in Table 7.

Table 7: Observed wind speed data and predicted data.

Time (min)
Observed value (m/s)
Predicted value (m/s)
Relative error (%)
Predicted value (m/s)
Relative error (%)

a1
b1

10
10.4900
10.0801
3.9561
10.5101
0.1823

20
10.3700
9.9783
3.7762
10.4011
0.2630

30
10.6900
10.1024
5.4902
10.4621
2.1476

40
10.2100
10.8802
6.5374
10.6202
4.0235

50
10.3000
9.1424
11.2402
10.1531
1.4947

60
9.8000
10.3508
5.6401
10.2210
4.2446

70
9.3000
8.6881
6.5810
9.6032
3.2585

80
9.4900
8.1883
13.7201
9.3163
1.8295

90
10.1100
9.6013
5.0321
9.7631
3.4282

100
9.2100
10.4726
13.7200
10.0300
8.9132

110
8.5500
7.2082
15.6903
8.8621
3.6533

120
9.0000
7.0824
21.3101
8.5470
5.0347

a2
b2

10
10.4900
9.6930
7.5977
10.4785
0.1095

20
10.3700
10.4006
0.2955
10.4249
0.5292

30
10.6900
10.0489
5.9968
10.4992
1.7845

40
10.2100
10.5699
3.5250
10.3357
1.2311

50
10.3000
10.2559
0.4285
10.2518
0.4675

60
9.8000
10.1445
3.5151
10.1132
3.1960

70
9.3000
9.2583
0.4488
10.0703
8.2826

80
9.4900
9.8944
4.2609
9.9547
4.8963

90
10.1100
10.6937
5.7739
9.7503
3.5580

100
9.2100
11.4420
24.2351
9.5212
3.3787

110
8.5500
11.8315
38.3804
9.1823
7.3954

120
9.0000
11.2543
25.0475
9.3012
3.3463

a3
b3

10
10.4900
10.9775
4.6468
10.7194
2.1870

20
10.3700
10.4931
1.1872
10.4277
0.5567

30
10.6900
10.3878
2.8269
10.4168
2.5559

40
10.2100
13.7393
34.5672
10.1893
0.2032

50
10.3000
9.8085
4.7716
10.2918
0.0793

60
9.8000
12.1826
24.3126
10.0993
3.0540

70
9.3000
9.7924
5.2943
9.8370
5.7739

80
9.4900
9.3521
1.4529
9.6689
1.8855

90
10.1100
14.5803
44.2168
9.7772
3.2920

100
9.2100
14.6095
58.6264
9.5805
4.0233

110
8.5500
8.6425
0.0821
9.3442
9.2884

120
9.0000
9.7379
8.1988
9.3314
3.6817

a4
b4

10
10.4900
8.9295
14.8759
10.1137
3.5868

20
10.3700
9.2037
11.2466
9.7344
6.1296

30
10.6900
8.6494
19.0887
10.3308
3.3597

40
10.2100
8.5516
16.2426
9.9803
2.2501

50
10.3000
9.3038
9.6717
10.5685
2.6064

60
9.8000
8.7467
10.7479
10.1183
3.2477

70
9.3000
8.7126
6.3161
9.7779
5.1391

80
9.4900
8.6188
9.1802
9.8413
3.7021

90
10.1100
9.4713
6.3176
10.9886
8.6901

100
9.2100
9.3204
1.1992
8.8816
3.5657

110
8.5500
9.5029
11.1452
8.2497
3.5118

120
9.0000
9.6909
7.6768
8.8558
1.6017

a5
b5

10
10.4900
10.1227
3.5017
10.4227
0.6412

20
10.3700
10.5150
1.3985
10.4890
1.1473

30
10.6900
10.3714
2.9805
10.6268
0.5909

40
10.2100
10.1625
0.4652
10.5638
3.4649

50
10.3000
10.5236
2.1705
10.4778
1.7260

60
9.8000
10.5366
7.5159
10.1741
3.8174

70
9.3000
9.8748
6.1807
9.8224
5.6174

80
9.4900
9.4500
0.4217
9.6744
1.9432

90
10.1100
9.7279
3.7794
9.8950
2.1267

100
9.2100
8.9728
2.5755
9.6195
4.4458

110
8.5500
9.3838
9.7519
9.2464
8.1455

120
9.0000
9.3328
3.6973
9.0710
0.7889

Figures 1–5 show that relative error of wind speed prediction by EKF neural network is much smaller than that by BP neural network, through observing the future wind speed prediction of 150 groups. As can be seen in Table 6, the prediction effects are largely different by different kinds of phase space reconstruction methods. Four performance indices, which are Mean Absolute Error (MAE), Mean Relative Error (MRE), Mean Square Error (MSE), and Sum of Squared Error (SSE), of EKF neural network, are also far less than those of corresponding general neural network.

Apparently, EKF neural network can solve the inconsistency problem of phase space reconstruction and approximate chaotic time series well through subspace. The neural network model based on EKF has outstanding adaptability, so it can predict the wind speed chaotic time series with higher precision, compared with BP neural network.

Furthermore, we can conclude that in Table 7, prediction accuracy of EKF neural network is higher than that of BP neural network, by comparing the prediction performance of wind speed in the next 10 min, 20 min, 30 min, and up to, 120 min. It demonstrates that EKF neural network model, which has better dynamic adaptability, can better the prediction of wind speed time series with nonlinear chaotic characteristics. Therefore, the proposed phase space reconstruction method of the adaptive evolution approximation in this paper is an effective approach.

5. Conclusion and Further Work

The phase space reconstruction cannot meet characteristics of the completeness and independence, and the results with different reconstruction methods are obviously inconsistent. The reconstructed phase space is a subspace of the ideal space. If a subspace approximation can make the real-time dynamic evolution, then the initial constructed phase space, for which the evolution is adaptive subspace approximation, can finally approximate to the ideal phase space much better.

In this paper, neural network model based on nonlinear Kalman filter is established, by dynamic adaptivity of nonlinear Kalman filter. The model will add new samples in real time and gradually eliminate previous data, as a moving samples window, and the evolution of the training sample continually updates weights and thresholds of the neural network. As a result, adaptive subspace approximation is implemented by reconstructed incomplete phase space.

The optimized plan, which combines the nonlinear Kalman filter with neural network, sufficiently utilizes the nonlinear approximation capability of neural network and dynamic adaptive ability of real-time update correction of nonlinear Kalman filter. Consequently, it can realize subspace adaptive evolution approximation and solve the inconsistency problem of phase space reconstruction. Therefore, it is a nice direction in research into chaotic prediction. Future research can be performed in a number of areas. It provides a good technical support in studying problems of meteorology, hydrology, and finance fields.

Acknowledgments

This work was supported by the National Science Foundation of China (no. 51075418), the National Science Foundation of China (no. 61174015), Chongqing CMEC Foundations of China (no. CSTC 2013jjB40007), and Chongqing Scientific Personnel Training Plan of China (no. CSTC 2013kjrc-qnrc40008).

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