The paper investigates the application of inversion of a radial basis function network (RBFN) to nonlinear control problems for which the structure of the nonlinearity is unknown. Initially, the RBF network is trained to learn the forward dynamics of the plant. Two different controller structures are then proposed based on this identified RBFN model. In one scheme, a feedback control law is der...

This paper is devoted to the problem of sampling Gaussian fields in high dimension. Solutions exist for two specific structures of inverse covariance : sparse and circulant. The proposed approach is valid in a more general case and especially as it emerges in inverse problems. It relies on a perturbation-optimization principle: adequate stochastic perturbation of a criterion and optimization of...

In the seismic inversion model, as the neural network algorithm there are some problems, the convergence bad, accuracy is not high. This paper presents a seismic inversion based on artificial fish Swarm Optimization neural network models. First to initialize fish mapping of chaos optimization and ergodicity of artificial fish-swarm search and adaptive strategies of artificial fish-swarm algorit...

This paper describes a few mild design constraints which permit rapid adaptation of modeling code for linear wave propagation to imaging/inversion or design optimization applications, retaining parallelism and other performance enhancements of the underlying simulator. It also describes an abstract software framework preserving the modularity of both optimization and modeling software in buildi...

In this paper, first a great number of inverse problems which arise in instrumentation, in computer imaging systems and in computer vision are presented. Then a common general forward modeling for them is given and the corresponding inversion problem is presented. Then, after showing the inadequacy of the classical analytical and least square methods for these ill posed inverse problems, a Baye...

We give an O∗(2k)-time algorithm for STEINER TREE using polynomial space, where k is the number of terminals. To obtain this result, we apply Möbius inversion, also known as Inclusion-Exclusion, to the famous Dreyfus-Wagner recurrence. Among our results are also polynomial-space O∗(2n) algorithms for several NP-complete spanning tree and partition problems. With these results, we solve open pro...

We consider the problem of reconstmcting a continuous function on R" from certain values of its spherical means. A novel aspect of our approach is the use of geometric inversion to recast the inverse spherical mean problem as an inverse Radon transform problem. W define WO spherical mean inverse problems the entire problem and the causal problem. We then present a dual filtered backprojection a...

In a variety of scientific applications we wish to characterize physical system using measurements or observations. This often requires us solve an inverse problem, which usually has non-unique solutions so uncertainty must be quantified in order define the family all possible solutions. Bayesian inference provides powerful theoretical framework defines set problems, and variational is method p...

Probabilistic inversion within a multiple-point statistics framework is still computationally prohibitive for large-scale problems. To partly address this, we introduce and evaluate a new training-image based simulation and inversion approach for complex geologic media. Our approach relies on a deep neural network of the spatial generative adversarial network (SGAN) type. After training using a...

Phylogeny reconstruction from gene-order data has attracted much attention over the last few years. The two software packages used for that purpose, BPAnalysis and GRAPPA, both use so-called breakpoint medians in their computations. Some of our past results indicate that using inversion scores rather than breakpoint scores in evaluating trees leads to the selection of better trees. On that basi...