Neural Networks for EMC Modeling of Airplanes Vlastimil Koudelka Department of Radio Electronics FEKT BUT Metz,

2 Outline  Introduction to artificial neural networks (ANNs)  Neural networks abilities  Evaluation of EM immunity of layered str.  Application of neural classifier in EMC  Regression neural network based optimization  Related problems  Conclusion

3 Introduction to ANNs (1)  Highly parallel structures  Basic element: Neuron  Organized to the layers  Adaptive nonlinear mapping (learning)  Nonlinear separable classification  Optimization features  W 1,2 (2) W 2,2 (2) W 3,2 (2) b 2 (2) f(n) n

4 Introduction to ANNs (2) RegressionClassification  Multi layered perceptron (MLP)  Radial basis network (RBF)  General regression network (GRN)  MLP  Probabilistic NN (PNN)  Self organizing map (SOM)

5 Introduction to ANNs (3) Optimization  Self organizing map  Self adopted GRNN  Hopfield neural network

6 NN applications: objectives and motivations  Behavioral modeling: continuous models, computational efficiency  Neural models: composite materials, equipment input impedances, aircraft fuel gauge, field levels  Pre-processing, post-processing and optimization tools  Offline training / Online responses  Suitable for direct parallel implementation  Noise suppression (Bayesian regularization)  Multidimensionality, adaptability, generalization, robustness

7 Evaluation of EM immunity of layered str. (1)  At three virtual probes the electromagnetic fields values are estimated by ANN  Harmonic and pulsed wave excitations (Gaussian pulse)  MLP, RBF, PNN

8 Evaluation of EM immunity of layered str. (2) Harmonic wavesPulsed waves  Dependencies of EM field values on the electrical parameters  Pulsed waves: the electric field intensity is expressed in its effective values to respect the mean stress of a virtual device.  MLP, RBF, PNN

9 Application of neural classifier in EMC Probability density function of exceeding prescribed limit  Classification of structures with various electrical parameters  Probability of EM structure successfulness is estimated by ANN  Probabilistic neural network

10 Optimization example 1) Initial set of trial solutions consisting of n+1 samples 2) Founded minima of the regression surface is taken as a new training pattern 3) After several iterations the GRNN is well adapted to unknown function f (x)

11 Related problems  Black box modeling (interpretation of NN weights and biases)  Validation techniques: cross validation NN error estimation (perturbation analyses)  Training set compilation: preprocessing and initialization  A number of neurons: clustering problem, regularization  Efficiency and performance of training algorithms: Benchmarks  Stability and robustness of dynamical NNs

12 Conclusion  Regression, classification, optimization  Computational efficiency, good generalization abilities  Pre-processing, post-processing and optimization tools  Wide area of applications (neural network adaptation)  Shielding efficiency, EM structure classification, material modeling  Black box modeling, validation, benchmarking

13 Contact Department of Radio Electronics, Brno University of Technology Purkynova 118, Brno, Czech Republic Tel: Fax: Vlastimil Koudelka

page 14 This work was supported by the project CZ.1.07/2.3.00/ Communication Systems for Emerging Frequency Bands