THE RESEARCH OF PREDICTION OF PESTS BASED ON FUZZY RBF NEURAL

NETWORK

YAN-LING WEI Department of Information Engineering

Liu Zhou Vocational &Technical College LiuZhou 545006, China

FEI-YING LIN Department of Finance and Economy GuangXi University of Technology

LiuZhou 545006, China

Abstract—The prediction for pests is usually amphibious, relevant, complicated, and nonlinear. The neural network has the problem of decreasing generalization ability in the prediction of small samples. This paper presents a method of the prediction of pests based on fuzzy RBF neural network. A learning algorithm of adjusting the center, width and weight of the RBF is put forward. The use of fuzzy clustering technique for data pre-processing and the use of RBF neural network for nonlinear prediction have solved the problem of the ambiguity, relevance and non-linear of prediction of pests. The simulation results show that the outcomes of the predictions of pests based on fuzzy RBF neural network are accurate. This method is simple and practical. Especially in the condition of small samples or larger relevance among samples, the use of the method can achieve better results.

Keywords- RBF neural network; fuzzy clustering; pests; prediction

I. INTRODUCTION The impact of epidemic diseases and insect pests are many

factors, these factors on the occurrence of epidemic diseases and insect pests with amphibious, relevance, complexity, non-linear characteristics, so the traditional methods of mathematical statistics during the prediction have more restrictions[1-2]. Since ANN (artificial neural network)adapt to solve non-linear prediction, Many scholars to use BP network model for nonlinear prediction systems[3],directly the ANN system introduces to agriculture, pests prediction has become its an area of applied research. Some scholars make use of neural network prediction of the pests studied [4-6], comparing with conventional and traditional methods, whether in the prediction accuracy or real-time have greatly improved. With the ANN method generally used in BP algorithm, the later developing radial basis function RBF network has good generalization ability, and learning speed faster than the ordinary BP algorithm is much faster. Research in recent years showed that: the approximation capability, sorting capability (pattern recognition) and learning speed of RBF, etc. are better than BP network, the selection of RBF neural network as a predicting tool.

Less in the samples, the sample of the larger inter-related circumstances, in order to improve the accuracy of the study

sample, so that the measurement data of a sample closer to objective reality, the measurement is related to the input sample prior to establish fuzzy relation matrix, which to be treated a group of fuzzy neural network as a vector of input samples, so that will be able to deal with the importation of samples of the actual situation is closer to the objective [7]. Literature [8] used this method to predict, from 6 sets of data for the sample set. After denoising the data from t(R) in the first four sets of input sample set, using the set of data 5,6 for testing. The simulation results with measured results are in full compliance with, receiving good results. This shows that based on fuzzy clustering neural network applied to a small sample of neural networks can improve the efficiency and accuracy. To small sample of the characteristics of pests prediction puts forward a fuzzy RBF neural network predicting method pests. A learning algorithm of adjusting the center, width and weight of the RBF is put forward. The use of fuzzy clustering technique for data pre-processing and the use of RBF neural network for nonlinear prediction have provided a rapidly, accurately new way to predict agricultural pests.

II. RBF NEURAL NETWORK LEARNING ALGORITHM

A. The Basic Principles of RBF Neural Network RBF network that is Radial Basis Function Neural

Network, base on the theory of function approximation and the structure of a network before. It is made up of the input layer, hidden layer and output layer the three-tier network[9-10]. Training sample set is expressed as:

.,...,1},,{ )()( nidxD ii == Where x is the input signal, d is the label signal, and n is the number of input signals. Input layer is made up of the signal source node. The second layer is the hidden layer, whose transformation function makes use of RBF. Radial Basis Function has many forms of Gaussian function in this check. The role of the hidden layer is to achieve a non-linear transform, the output of hidden layer nodes is

)2

exp()( 2

2

i

i

i

Cxx

σφ

−−= mi ,,2,1= (1)

978-1-4244-4507-3/09/$25.00 ©2009 IEEE

Where )(xiφ is layer hidden node i the output, )(xiφ ∈［0,1］, iC And iσ that is the ith basis function centers and width of the vector, m is the number of nodes in hidden layer, that is, the number of radial basis function. RBF network output of output layer nodes can be expressed as:

)2(,...,2,1)(1

ljxwy i

m

iij =−=∑

=θφ

Where jy is the output of output layer node j, iw is the ith basis function of the weight or incentive strength, θ is the output layer node thresholds. l is the output nodes.

RBF neural networks generally use the BP algorithm to adjust weights, in order to better extract the characteristics of the problem, following to put forward a RBF neural network learning algorithm.

B. A Learning Algorithm of RBF Neural Network From formula (2) to know, RBF neural network output

value is decided by the basis function center, widths and the weights of the basis function. Width of basis function is a great impact to approximation ability of the network, and also RBF center should be able to cover the entire input space. Therefore, the RBF network model to establish the key is to determine the RBF centers, widths and weights of basis functions. To this end, put forward a method to RBF neural network on the basis function center, weight and width to adjust the learning algorithm.

,)()( ii yd −=ε

,1 iiw εφη=Δ

,)()( 2

2

2i

ii

k k

ii

xcywσφ

φεησ −−=Δ∑

.)()(3i

ii

k k

ii

xcywcσφ

φεη −−=Δ∑

Where ε is a network error, 321 ,, ηηη are the learning rates.

III. FUZZY RBF NEURAL NETWORK

A. Fuzzy RBF Neural Network Architecture Fuzzy RBF neural network (hereinafter referred to as

Algorithm 1) structure as shown in Figure 1. Which enter the original value of ),,,,,,( 21 nk vvvvv ……= Fuzzy pre-processing of the initial value after the RBF neural network input vector ),21 ,,,,,( nk xxxxx ……= Hidden layer output vector for the ))(),...,(),(()( 21 xxxx mφφφφ = , Output layer output

vector for the ),,,,,( 21 lk yyyyy ……= .

B. Fuzzy Pre-processing Data The fuzzy pre-processing data steps are following.

Step1: Establishment fuzzy similarity matrix R

Establish absolute value decreases fuzzy similarity matrix R. This method understand easily and clearly .Its formula as following

⎪⎩

⎪⎨

⎧

≠−−

==

∑=

m

kjkik

ij jixxC

jir

1

||1

1

Where, ikx , jkx are the attribute values. C is a constant, so that

≤ ijr ≤1. i, j=1,2,...n. where n is sample size, m is the number of sample properties.

Step2: Ask for fuzzy equivalent matrix t(R)

Through seeking the adoption of the transitive closure R, namely R square was 2RR*R = , Square again 422* RRR = , Until kk RR 2= the fuzzy equivalent matrix is kk RRRt 2)( == , k∈N. Acquiring fuzzy equivalent matrix is as a sample of data neural network.

IV. FUZZY RBF NEURAL NETWORK PESTS PREDICTING In this paper, the fuzzy RBF neural networks are better able

to extract the characteristics of the problem, applicable to small samples, and can improve neural networks efficiency and accuracy. The method is applied pests prediction, for 21 years against the degree of second-generation corn borer (d, insect-infected trees rates %) data from the literature [11]. The first 1~16 years of data as a neural network to study sample, 17~21 years as a sample prediction.

A. Pre-processing of Raw Data 1) Selection of Evaluation Target [11]

The impact of second-generation corn borer insect-infected trees at the rate of d(%) of the main factors [11]: R1 for the spring and summer planting area of more than corn, R2 remnants for the volume of the first generation (head/100 trees), R3 as the first ten days of July temperature and humidity coefficient, R4 as the rainfall (mm) of the first ten days of July, R5 as the average temperature(℃).of the first ten days of July.

xn

...

...

...

...

...

x1 x2

...

xk

input layer

output layer

...

)(1 xϕ )(2 xϕ )(xkϕ )(xmϕ

1y 2y ly

v1 ...

hidden layer

vnv2 vk

The use of fuzzy clustering technique for data pre-processing

Figure 1. The Structure of Algorithm 1

2) Predicting Indication of the Standard of Classification and the Value of the Raw Data [11]

Predicting object d is divided into five: 1, small occurred; 2, the occurrence of light-middle; 3, occurred in the middle; 4, occurred towards the heavy middle; 5, big happened. Predicting factor Ri (i = 1,2,3,4,5) uses poly graph points combined experience and divides into five, and its grading standards, such as Table Ⅰ . According to Table Ⅰ the prediction indicators of the classification criteria, the watch data of raw prediction indicator of 21-year as grade value, as shown in Table Ⅱ. After the classification of the data as a data network to deal with samples.

3) Fuzzy Equivalence Relation Matrix t(R) If the rotation of each type of sample data input focus, it

will lead to emergence of network training concussion longer training time making. 1~16 years of sample data in the first three and two is too concentrated, it is appropriate to adjust the order of input, using different types of sample data cross inputting. As a result of indicators different dimension and units, this paper uses the method of the smallest and the largest to after the classified data to the standardization of processing [7]. In this paper, the number of the attributes for the m =5, i, j = 1,2,...,21, C = 0.2, calculation of fuzzy relation matrix R matrix and fuzzy equivalence relation matrix t(R). Get fuzzy equivalent matrix t(R).

B. Simulation Results and Analysis The use of t(R) group of the top 16 is the importation of the

study sample set. Selecting from the experience of the number of hidden neurons is 12. The impact of second-generation corn borer insect-infected trees at the rate of d is divided into 1 to 5. The target output pattern is (00001), (00010), (00100), (01000), (10000), and the number of output neurons is 5, transfer function for the S-type function, using fuzzy RBF neural network training. Make use of t(R) behind the five groups for testing, predictive value and simulation results the corresponding as shown in Table Ⅲ . Taking a=a>0.5, predictive value and the simulation results are in full compliance with. The experimental system, after 12 iterations to achieve a total error of about 0.206584 (see Figure 2). As we can see from Figure 2, the training results of Algorithm 1, after 12 times of training, the convergence results are satisfactory, the network convergence and stability of better and faster training.

Use the literature [4] of the RBF algorithm (the Algorithm 2),directly using after the classification data for samples, as Algorithm 1 with the same conditions for training. Algorithm 2 Training results shows in Figure 3,predictive value and simulation results the corresponding as shown in Table Ⅲ . Taking the same a=a>0.5, simulation results of the first, second, third and fourth samples of the prediction is correct, but the fifth sample of the prediction is error. We can see that Algorithm 1 using the prediction accuracy rate was significantly higher than Algorithm 2 prediction accuracy rate.

TABLE I. PREDICTION FACTOR CLASSIFICATION

TABLE II. THE VALUE OF THE RAW DATA CLASSIFICATION

prediction factors R1 R2 R3 R4 R5 d

1 5 4 5 5 2 5

2 5 4 1 2 5 4

3 4 1 3 4 3 2

4 5 2 2 2 2 3

5 4 1 1 1 1 1

6 3 2 3 3 3 3

7 3 3 5 5 2 3

8 4 2 3 3 3 3

9 3 2 2 2 2 3

10 3 2 5 5 2 3

11 2 1 1 2 1 2

12 3 3 2 3 4 3

13 3 3 4 5 5 3

14 3 5 4 4 1 4

15 2 1 3 3 3 2

16 2 3 5 5 2 3

17 2 2 2 2 3 2

18 2 3 5 5 2 3

19 2 3 1 2 2 2

20 2 3 1 2 2 2

21 2 2 4 5 3 2

0 2 4 6 8 10 1210

-1

100

101

102

12 Epochs

Tra

inin

g-B

lue

Goa

l-Bla

ck

Performance is 0.206584, Goal is 0.2

Figure 2. The training result of Algorithm 1

grade R1 R2 R3 R4 R5 d

1 <0.02 <32 <1.7 <7 <23.4 <10 2 0.02～0.14 32～49 1.7～2.3 7～56 23.4～25.5 10.0～20 3 0.15～8.66 50～97 2.4～4.7 57～99 25.6～26.8 20.1～40 4 8.67～9.43 98～1524.8～5.5 100～134 26.9～29.1 40.1～60 5 >9.43 >152 >5.5 >134 >29.1 >60

0 1 2 3 4 5 610

-1

100

101

102

6 Epochs

Tra

inin

g-B

lue

Goa

l-Bla

ck

Performance is 3.80917, Goal is 0.2

Figure 3. The training result of Algorithm 2

Due to rapid growth of pests, the resistance was enhanced as the size and weight of pests increased, and thus their control cost also increased. So pests forecasting requires high timeliness. The simulation results of the study proves that Algorithm 1 meets the requirements of real-time of pests forecasting.

V. CONCLUSION This paper puts forward fuzzy RBF neural network for

prediction of crop pests.The simulation results proves that this method is feasible. This method combinates the advantages of fuzzy theory and of RBF neural network.It has good application value for fast computation and accurate results. It can also give good results when the number of samples is few and a strong relationship exists among the samples.The predictive precision increases with the amount of original data.

ACKNOWLEDGMENT This paper is supported by Guangxi Nature Science

Foundation (0481016), Guangxi Scientific Research and Technology Development Foundation (0992006-13).

REFERENCES [1] X.B. Zhang, L.Y.Zhou, “Basic knowledge of pest predicting”, Insects

knowledge, 1995,32 (11) :55-60. [2] J.Y.Mou, Insect ecology and agricultural pest predicting , Beijing: China

Agriculture Press, 1990 [3] Srinivasan B, Prasad U Rao N J, “Back propagation through adjioints

for the identification of nonlinear dynamic systems using recurrent neural models”, IEEE Transactions on Neural Networks, 1994,5 (2):213-228.

[4] B. L. Fu, “RBF network prediction of plant diseases and insect pests in agriculture applied research”, Anhui Agricultural Science, 2008,36 (1) :388-389 .

[5] X. Wang, J.L.Lu, X. M. Fu, “Rice pests intelligent prediction model and its application”,Agricultural Engineering of Anhui, 2008,24 (7) :141-143

[6] N. S. Liu, F. X. Liu, “BP artificial neural network prediction of plant pests in the application”. Anhui Agricultural Science, 2007, 35 (25) : 7765-7766 .

[7] S. C. Yuan, D. H. Liu, H. B. Liu,”Based on fuzzy pre-treatment clustering neural network model design”, Xi'an University of Architecture and Technology (Natural Science Edition), 2005, 37 (2) : 274-277 .

[8] H. Rao, M. F. Fu, M. X. Xie, ”Based on fuzzy clustering neural network fault diagnosis method”, Micro-Computer Information, 2007, 23 (1) : 196-197, 285 .

[9] J. Moody, C. Darken, “Fast Learning in networks of locally-tuned processing units”, Neural Computation, 1989(1): 281-294.

[10] Simon Haykin, Neural Networks: A Comprehensive Foundation, S. W. Ye and Z.Shi Transl, Beijin：Machinery Industry Press，2004: 137-186．

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TABLE Ⅲ. THE PREDICATIVE VALUE AND THE SIMULATION RESULTS THE CORRESPONDING

Samples Predictive value The Algorithm 1 simulation results The Algorithm 2 simulation results

1 0 0 0 1 0 0.0559 0.0406 0.2484 0.6403 0.0148 -0.10028 0.13448 0.42845 0.58629 -0.04894

2 0 0 1 0 0 -0.0435 -0.0335 1.1679 -0.0803 -0.0106 0.000432 0.2777 0.52248 0.1528 0.046588

3 0 0 0 1 0 0.0448 0.036 0.0674 0.8388 0.013 -0.11169 0.33944 0.19952 0.60005 -0.02731

4 0 0 0 1 0 0.0448 0.036 0.0674 0.8388 0.013 -0.11169 0.33944 0.19952 0.60005 -0.02731