Soft Computing for Recognition based on Biometrics - download pdf or read online

By Patricia Melin, Witold Pedrycz

ISBN-10: 3642151108

ISBN-13: 9783642151101

We describe during this booklet, bio-inspired types and purposes of hybrid clever platforms utilizing delicate computing options for picture research and development reputation in accordance with biometrics and different details resources. gentle Computing (SC) includes a number of clever computing paradigms, together with fuzzy common sense, neural networks, and bio-inspired optimization algo-rithms, that are used to supply strong hybrid clever platforms. The e-book is geared up in 5 major elements, which comprise a bunch of papers round an identical topic.

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The kernel is related to the Ø function by: K ( xi , x) = φ ( xi ) ⋅ φ ( x) (21) In this work the radial basis function (RBF) was used. RBF is defined as: K ( xi , x j ) = e − xi − x j 2 σ (22) Classification of the test sample x is then performed by: ⎛ N ⎞ y = sign ⎜ ∑ α i vi K ( xi , x) + b ⎟ , ⎝ i=1 ⎠ where N is the number of training samples, (23) vi is the class label, αi a Lagrangian multiplier, the elements xi for which αi>0 are the support vectors, and K(xi,x) is the function kernel. In this work a Gaussian radial basis function was used.

The fuzzy algorithm is similar to the crisp version in the sense that it must also search the labeled sample set for the k-nearest neighbors. Beyond obtaining these k samples, the procedures differ considerably [17]. , xn } be the set of n labeled samples. Also let ui (x) be the assigned membership of the vector x (to be computed), and uij be the membership in the ith class of the jth vector of the labeled sample set. The algorithm is as follows: BEGIN Input x, of unknown classification. Set K,1 ≤ K ≤ n.

Compute the output of the ANN, denoted by yp, by propagating each input pattern xp through the network in a forward direction. 3. - Compute the error between the desired output, d, and the output produced by the ANN y, this is given by Ep = 1 (d p ln − d p ln ) 2 . 2 4. - Adjust the connection weights according to the following rule, 44 E. Ramírez, O. Castillo, and J. Soria Wln[ t +1] = Wln[ t ] + βδ p ln x pl (1) In the above equation ߚ represents the learning rate, a parameter that is used to modulate the amount by which the connection weights can be modified at each iteration of the algorithm.

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Soft Computing for Recognition based on Biometrics by Patricia Melin, Witold Pedrycz


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