Adaptive Sampling with Mobile WSN: Simultaneous robot by Koushil Sreenath, M.F. Mysorewalla, Dan O. Popa, Frank L.

By Koushil Sreenath, M.F. Mysorewalla, Dan O. Popa, Frank L. Lewis

This informative textual content for graduate scholars, researchers and practitioners engaged on cellular instant sensor networks offers theoretical dependent algorithms with a spotlight in the direction of functional implementation.

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Additional info for Adaptive Sampling with Mobile WSN: Simultaneous robot localisation and mapping of paramagnetic spatio-temporal fields

Example text

Any continuous function can be approximated with RBF neural network according to universal approximation theorem. m0 Let Ð G : < ! < be an integrable bounded function such that G is continuous and 0, ai [ < and ci 2

6 4 Z1 ... 2 ð3:19Þ where Mn is an n  m matrix. 19) is simply ... @ A^n ¼ ð 1 g i ðX j Þ Þþ i m, j n Z1 1 0 A ¼ M nþ@ Z1 ... 0 Zn 1 A ð3:20Þ Zn where M n þ is the Moore–Penrose inverse of Mn. Moreover, the pseudo-inverse has a closed-form solution given by M n þ ¼ ðM n T M n ÞÀ1 M n T ð3:21Þ leading to a parameter estimate closed form, as follows: ... B BX B B n B 1 Á Á Á g ðX Þ Á Á Á g ðX Þ ð Þ A^n ¼ B i j m j B g i ðX j Þ B @ @ j¼1 0 11À1 CC CC CC CC AA gm ðX j Þ n X j¼1 1 1 ... 1 B B g ðX Þ ZjB B i j @ ...

Of samples 0 P(c) 100 0 1 0 50 100 Sample no. Error in c –1 0 0 Error in a 0 –2 0 50 Sample no. 2-Norm of error 0 0 5 0 200 400 Distance (units) 10 0 200 400 600 Distance (units) 10 5 0 50 No. of samples 5 0 100 0 20 40 60 No. of samples 0 50 Sample no. P(c) 10 50 Sample no. 5 5 0 10 5 50 Sample no. P(b) 0 5 50 100 Sample no. 10 0 0 50 Sample no. c 1 0 50 Sample no. 5 5 0 10 0 50 100 Sample no. b Error covariances:P(a) 10 10 5 0 100 2 0 50 100 Sample no. 2-Norm of error 2-Norm of error 5 10 2-Norm of error Error in b 2-Norm of error covariance –2 0 10 0 50 100 Sample no.

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