IMPLEMENT OF ELECTROMAGNETIC PULSE EFFECTS EVALUATION OF RADAR SYSTEM FUNCTION MODULE

WAN-ZHEN ZHOU, JIAN-XIA WANG, XIN-XIN LIU, JING-FU XUE

College of Information Science & Engineering, Hebei University of Science and Technology Hebei Shijiazhuang 050018, China

E-MAIL:zhou_wanzhen@126.com,wang_jianxia@hebust.edu.cn

Abstract: Based on radar system function module 04-combination as

the research object, this paper expatiates the establish process of the cross-neural network module of this module electromagnetic pulse action particularly, and designs evaluate system of this kind of electron system electromagnetic pulse effect, using the detailed process that BP-RBF cross neural network evaluation algorithm predicts this module. Finally, according to the evaluate experiment of 08- combination testing the effect of this evaluate system, results indicate that it is feasible that using this evaluate system evaluates electromagnetic pulse effect of electronic system.

Key words: Radar systems; Electromagnetic pulse effect; Evaluation

system; BP-RBF cross-neural network

1. Introduction

Electromagnetic pulse effect of an electronic system is defined as an collection that electronic systems may be appear a variety of states or faults in sundry expected action of electromagnetic pulse. Studying electromagnetic pulse action evaluation of electronic system is to find out the laws and cases of these states, so as to direct electromagnetic pulse protection design of electronic system. These states can be summarized as: no influence; when there is pulse, it has influence, but it returns to normal after the pulse is removed; a pulse causes system halted and not work, so it needs to reset by hands and can resume normal work, and it is a soft injury; it is burned and hard damage.

2. Establishment of the evaluation module

A general question can be resolved by the error back-propagation (BP) network algorithm or radial basis function (RBF) network algorithm, but it is difficult to achieve satisfactory results using these two algorithms when

we come across evaluation of electronic system of electromagnetic pulse effects these complex, practical issues. Error back-propagation network training is slow, when the training times reach the maximum number of 1000, the network does not meet the desired objectives of the training error of 0.001. Radial Basis Function Network Algorithm adopts method that transforms the low-dimensional input data to high-dimensional space to simplify and optimize to premature end of the training network, and it does not meet the desired error of the training objective. As a result, this paper presents neural network cross-evaluation module, according to the characteristics of electromagnetic pulse effect evaluation of electronic system.

2.1. Cross-neural network evaluation module

The main factor that affects electronic system of electromagnetic pulse actions can be divided into two types: the square wave electromagnetic pulse width of electromagnetic environment equivalent, intensity and the damage threshold of various electronic devices in 04-combination.

In accordance with these two types we design the input value as two vectors: 1) first of all, we make a equivalent hazard source type, converting the different types of hazard sources into electromagnetic pulse hazard sources of square wave, this way we only need to consider the pulse width and intensity, so we design the first vector for two dimensional vector(width, intensity); 2) selecting the damage threshold of electronic devices is very important, such as injury-voltage threshold, injury-current damage threshold and injury-power threshold.

We propose cross neural network evaluation module according to the characteristics of evaluation of electromagnetic pulse effects of electronic system, as shown in Figure 1.

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Proceedings of the 2010 International Conference on Wavelet Analysis and Pattern Recognition, Qingdao, 11-14 July 2010

Figure1. Cross-neural network evaluation module

As shown in Figure 1 for the three-tier cross-neural network evaluation module, the first layer is dealt with by BP algorithm, and the second layer by RBF algorithm, this module is defined as BP-RBF cross-neural network algorithm module; if the first layer is dealt with by RBF algorithm and the second layer by BP algorithm, the module will be defined as RBF-BP cross-neural network algorithm module.

2.2. Comparison of BP-RBF and RBF-BP cross-neural network evaluation module

The following part of the paper we will test and compare these two kinds of evaluation algorithms respectively according to the part of experimental data of 04-combination. BP-RBF cross-neural network evaluation algorithm, the error of training objectives is 0.001, the maximum training number is 1000 and leaning rate is 0.05, training the network and the network circles eight times, reaches the training accuracy. Whereas RBF-BP cross-neural network evaluation module reaching the training accuracy

needs fourteen times cycles. Compared with error back-propagation network evaluation module or radial basis function network evaluation module, cross-neural network evaluation module has obvious advantages in training of speed and precision, especially BP-RBF cross-neural network module. By the comparison of the above evaluation module, we select the optimal cross-neural network module as evaluation module of electromagnetic pulse action of electronic system.

3. Realization of evaluation system

3.1. Processing of the sample data

All the data collected is often not in the same order of magnitude, so we map the data collected to [-1, 1], treating normalized, in order to help improve the speed of the network training. The specific algorithm is:

2 min )/(max min 1Pn p p p p× (1) In the equation above, p is a set of data collected, and

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Proceedings of the 2010 International Conference on Wavelet Analysis and Pattern Recognition, Qingdao, 11-14 July 2010

min p max p are respectively the minimum and maximum

values of this set of data, and so Pn is the data of has mapped.

Matlab provides a function mapminmax() that normalizes the data, and its call type is:

[ , _ ] mapminmax( )Pn Ps P P= (2) Among them, P is the input vector, and Pn

denotes the input vector of normalization, and then _Ps P is the variable that needs deal with, and used to preserve the maximum and minimum values of specific factors in certain training samples.

The data in this paper used is radar system data in all of the combination experiments, and we take 04-combination experimental data as leaning and training sample of neural network, and 08-combination as the test sample.

3.2. Training of the evaluation module

The training process of evaluation module is as follows: First of all, the experimental data is pre-processed using

the normalized method, so the data not in the same order of magnitude is mapped to [-1, 1].

Then, the electromagnetic pulse effects evaluation network of radar system is initialized, and the network training parameters are set up, and the expected error is selected as 0.0001 and maximum training times are 1000, and learning rate is 0.05.

Finally, the 04-combination data is selected as training

sample to test evaluation network and preserve the parameters of this combination evaluation network (input weight matrix, the layer weight matrix, and threshold vector).

The curve of the error squared sum is shown in figure 2 during the training process. We can see from the curve that the error square sum decreases gradually as the training times increase, and the network is trained after 33 times, the error square sum of the network achieves target error requirements.

Figure2. Training process

As can be seen from table 1, the training results and the desired output is almost exactly the same.

TABLE1. TRAINING RESULTS

sample Evaluation standard

1 2 3 4 5 6 7 8 9

E1 1.000 0 1.000 0 1.000 0 0.999 9 0.909 7 0.999 4 1.000 7 0.999 8 1.000 0

E2 1.000 0 1.000 0 1.000 0 1.000 0 1.000 0 1.000 0 1.000 5 1.000 0 0.999 9

E3 1.000 0 1.000 0 1.000 0 1.000 0 1.000 1 1.000 3 1.000 3 1.000 1 0.999 9

E4 1.000 1 1.000 0 1.000 1 1.000 0 1.000 2 1.000 5 0.999 5 0.999 7 0.999 9

3.3. Function module of evaluation system

The design using Microsoft VC++ language designs the main interface to achieve human-machine function, and builds Microsoft VC++ and Matlab application program interface complete to the training of the evaluation network

and the evaluation prediction.

The main function module of this soft is network training module and evaluation prediction module, and functions of each module are as follows:

1) Network training module First of all, electromagnetic pulse effects evaluation

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Proceedings of the 2010 International Conference on Wavelet Analysis and Pattern Recognition, Qingdao, 11-14 July 2010

network is established to set up the network structure properties and network training parameters (leaning rate, target error, and maximum cycle times). Then, we take a combination of data as samples for training, and finally save the evaluation network parameter of this combination.

2) Evaluation and prediction module Evaluation and prediction module is to evaluate only

after the completion of the training module. First, evaluation network that has been well trained is loaded, and then, we enter the combination data to be evaluated, and finally, click run to obtain the predict outcome.

4. Evaluation results analysis of evaluation system used in 08-combination

This article refers to statistics data in electronic components damage test of some system to model. The evaluation system that based on Matlab neural network toolbox is established using Microsoft VC++ to realize electromagnetic pulse effects evaluation of radar system 08-combination using BP-RBF cross-neural network evaluation module.

08-combination data is selected as test samples to evaluate electromagnetic pulse effects of 08-combination using evaluation system, and the results are shown in Table 2 and Table 3.

TABLE2. ACTUAL RESULTS

sample Evaluation standard

1 2 3 4 5 6 7 8 9 10

E1 1.000 0 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

E2 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

E3 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

E4 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

TABLE3. PREDICTABLE RESULTS

sample Evaluation standard

1 2 3 4 5 6 7 8 9 10

E1 1.518 5 1.136 0 0.200 0 0.771 8 0.309 7 0.532 9 0.542 3 1.167 8 0.120 5 0.259 5

E2 0.288 5 0.209 3 0.522 7 0.254 1 0.194 5 0.644 5 0.423 7 0.137 0 0.594 3 0.474 4

E3 0.798 3 0.661 1 0.107 3 1.202 9 0.982 8 0.084 8 0.730 7 0.321 5 0.047 9 1.090 3

E4 0.570 3 0.524 6 0.048 1 0.707 5 0.007 7 0.982 4 1.653 4 0.281 8 0.556 2 0.022 7 According to the various states or the fault conditions

that electronic devices appear under the electromagnetic pulse effects, the anti-electromagnetic pulse ability of radar function module can be divided into four levels: high, medium, low and poor, is respectively represented as E1(high), E2(medium), E3(low), E4(poor).

The value of each sample's actual results are expressed with the four-dimensional vector, of which four elements represent the level of E1, E2, E3, E4, the value of the element formed by 1 and -1, 1 indicates selected, -1 no selected. For example, the actual results of the first sample

are {1, -1, -1, -1}, E1’s value 1 indicating E1 selected, so the level of the first sample is high.

Table 3 is the forecasting results for the evaluation of sample and the predicted results are represented with four-dimensional vector, element value close to 1(or larger values)indicating selected and close to -1 (or smaller values) not selected. For example, the predicted results of the first sample are {1.5185, -0.2885, -0.7983, -0.5703}, in which 1.5185 close to 1 representing E1 is selected, so the predicted level of the first sample is E1 corresponding to high.

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Through the comparison of the two tables, the forecast level and the actual level is basically the same and we can see that the evaluation network predicted results and actual results are almost identical.

5. Conclusion

The paper taking 04-combination and 08-combination as research objects, expounds detailing the establishing process of cross-neural network module in this model, and the detailed process that this model is trained and predicted using BP-RBF cross-neural network evaluation algorithm. We designed evaluation system with network training and predicted algorithm these two main functional modules, and finally tested the effects of this evaluation system through the evaluation experiments of 08-combination, and test results showed that: it is feasible that using this evaluation system evaluates electromagnetic pulse effects of electronic system.

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