In this paper we present a regularization approach to the training of all the network weights in cascadecorrelation type constructive neural networks. Especially, the case of regularizing the output neuron of the network is presented. In this case, the output weights are trained by employing a regularized objective function containing a penalty term which is proportional to the weight values of...

this paper describes an efficient edge-preserved regularization algorithm for downward continuation of magnetic data in detection of unexploded ordnance (uxo). the magnetic anomalies arising from multi-source uxo can overlap at a height over the ground surface, while causative sources may not be readily separated due to low level of signal-to-noise ratio of the observed data. to effectively the...

Fluvial systems carve the Earth's surface under influence of climate and tectonics. This process tends towards dynamic equilibrium conditions, in which rivers respond by regularizing their longitudinal profiles to a graded form. However, external forcings frequently interrupt this trend displacing system transient state from will evolve again with tendency In bedrock incising rivers, profile sh...

It is interesting to superimpose the Pauli–Villars regularization on the lattice regularization. We illustrate how this scheme works by evaluating the axial anomaly in a simple lattice fermion model, the Pauli–Villars Lagrangian with a gauge non-invariant Wilson term. The gauge non-invariance of the axial anomaly, caused by the Wilson term, is remedied by a compensation between Pauli–Villars re...

This study deals with the 3D recovering of magnetic susceptibility model by incorporating the sparsity-based constraints in the inversion algorithm. For this purpose, the area under prospect was divided into a large number of rectangular prisms in a mesh with unknown susceptibilities. Tikhonov cost functions with two sparsity functions were used to recover the smooth parts as well as the sharp ...

In this paper Tikhonov regularization for nonlinear illposed problems is investigated. The regularization term is characterized by a closed linear operator, permitting seminorm regularization in applications. Results for existence, stability, convergence and convergence rates of the solution of the regularized problem in terms of the noise level are given. An illustrating example involving para...

We propose a novel network traffic matrix decomposition method named Stable Principal Component Pursuit with FrequencyDomain Regularization (SPCP-FDR), which improves the Stable Principal Component Pursuit (SPCP) method by using a frequency-domain noise regularization function. An experiment demonstrates the feasibility of this new decomposition method. key words: Traffic Matrix, Stable Princip...

Truncated Singular Value Decomposition (TSVD) regularization method have been used by Zhao et al. [ " Kronecker product approximations for image restoration with new mean boundary conditions " (2011), Applied Mathematical Modelling, Vol. 36, pp. 225-237]. In this report, I propose an alternative regularization the Tikhonov method. The new regularization method gives better relative error when a...

In noise removal by the approach of regularization, the regularization parameter is global. Constructing the variational model min g ‖f − g‖L2(R) + αR(g),g is in some wavelets space. Through the wavelets pyramidal decompose and the different time-frequency properties between noise and signal, the regularization parameter is adaptively chosen, the different parameter is chosen in different level...

In this paper, we set forth a new family of regularizing filters based on the magnitude response function of Chebyshev-I lowpass filter. The corresponding regularization strategies for inverse problem are constructed. The optimum asymptotic order of the regularized solution is obtained by a priori choice of the regularization parameter. Finally, numerical results are given to demonstrate the ef...