Sharing and cooperation in geo-information technology

Aziz, T. Lukman

Bibliographic data

Monograph

Persistent identifier:: 856479470

Author:: Aziz, T. Lukman

Title:: Sharing and cooperation in geo-information technology

Sub title:: ISPRS Commission VI Symposium, April 15 - 17, 1999, Bandung, Indonesia

Scope:: 1 Online-Ressource (130 Seiten)

Year of publication:: 1999

Place of publication:: London

Publisher of the original:: RICS Books

Identifier (digital):: 856479470

Illustration:: Illustrationen, Diagramme

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: SPECIAL SESSION: EARTH MONITORING

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: THE COMMUNICATION CONTROL IN MUTUAL CONNECTED NETWORK BY RC-RBFN. Koji Okuhara, Haruhiro Fujita and Toshijiro Tanaka

Document type:: Monograph

Structure type:: Chapter

117 The i-th input vector with d-dimension is given to all input neurons. The j-th input neuron has the parameter. This parameter is given by the mean vector and the cocovariance matrix, where the cocovariance matrix is the matrix whose kl-th element is 2 (xj-md Z,'0c f - m,)> to the Lyapunov function. 3. REPRODUCTIVE CRITERIA OF NETWORK dE(w,</>) = ^ dE(w,</>) d Wj < Q dt d\V, dt PARAMETER given by the inverse matrix. The j-th input neuron outputs for the input vector, where the subscript denotes the transpose of the matrix. The input neuron does such output is called the radial basis function. The output is propagated to the output neurons through the synaptic weight. These are added up in the output neurons and We further extend CRBFN for adapting to the change of network environment. For this purpose, reproduction of parameter is considered. The update rule of parameter is given by 5 F ,im) d m m is outputted. The function approximation by neural network is regarded as that the nonlinear function is approximated by the output of the network. For this purpose, the approximation by the RBFN is realized by decreasing the total of squared error function E(w, (p)m~ z E( Xi , w, </>) where E (x, w ’</>) = is the squared error function related with the each output neuron. That is, RBFN must get the synaptic weights, parameters of the j-th radial basis function by learning. A *m l = A m J instead of equation for CRBFN, where the mean vector depending on each input vector. If the initial value satisfy then the update rule for the parameter of RCRBFN is just same to the update rule for the parameter of CRBFN. It is found that the parameter which is given by Z?0t ’ ntjvfoc, ~ m At ) Wx,)' s( *Xi ’ ntm)} = 0 The teaching signal can be detected as the convergence of the parameter. The algorithm of RCRBFN We have already proposed CRBFN, which is superior to RBFN. The learning algorithm of CRBFN is as follows; A A dE(w,(f)) w, = - £ . . k dE(w,tf>) m=- £ —^— d m. where the coefficient takes 1 or -1 depending on the sign STEP 1 The synaptic weight is updated by equation, the parameters by equation and equation. STEP 2 If the total of squared error function then stop the learning, otherwise go to next step. STEP 3 For all radial basis function, the parameter is updated by equation when the coefficient increase from 0 gradually. A wj = £ (a j M - £ y v M /u t Wk) Wj of the synaptic weight is given by We showed that the squared error function is equivalent dE(w,</>) C7, = -8 cr, STEP 4 If the value satisfying the fixed point increases by bifurcation, the j-th radial basis function is reproduced as the p-th radial basis function. Then the synaptic weight, the parameters take over ones of the j-th radial basis function, the parameter is given by the added point newly by bifurcation. Return to step 1.

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