Identification of an unstable ARMA equation.
Identification of critical curves. II. Discretization and numerical realization
We consider the finite element approximation of the identification problem, where one wishes to identify a curve along which a given solution of the boundary value problem possesses some specific property. We prove the convergence of FE-approximation and give some results of numerical tests.
Identification of hysteric control influence operators representing smart actuators. I: Formulation.
Identification of linear stochastic systems based on partial information.
Identification of optimal policies in Markov decision processes
In this note we focus attention on identifying optimal policies and on elimination suboptimal policies minimizing optimality criteria in discrete-time Markov decision processes with finite state space and compact action set. We present unified approach to value iteration algorithms that enables to generate lower and upper bounds on optimal values, as well as on the current policy. Using the modified value iterations it is possible to eliminate suboptimal actions and to identify an optimal policy...
Identification problem for stochastic models with application to carcinogenesis, cancer detection and radiation biology.
Identification with interruptions as an anti-bursting device
Identifications optimales de paramètres pour un système linéaire excité par un bruit gaussien
Identifikace lineárních soustav s násobnými póly
Identifying generalized Fitzhugh-Nagumo equation from a numerical solution of Hodgkin-Huxley model.
Image recall using a large scale generalized Brain-State-in-a-Box neural network
An image recall system using a large scale associative memory employing the generalized Brain-State-in-a-Box (gBSB) neural network model is proposed. The gBSB neural network can store binary vectors as stable equilibrium points. This property is used to store images in the gBSB memory. When a noisy image is presented as an input to the gBSB network, the gBSB net processes it to filter out the noise. The overlapping decomposition method is utilized to efficiently process images using their binary...
Imitation learning of car driving skills with decision trees and random forests
Machine learning is an appealing and useful approach to creating vehicle control algorithms, both for simulated and real vehicles. One common learning scenario that is often possible to apply is learning by imitation, in which the behavior of an exemplary driver provides training instances for a supervised learning algorithm. This article follows this approach in the domain of simulated car racing, using the TORCS simulator. In contrast to most prior work on imitation learning, a symbolic decision...
Immunotherapy with interleukin-2: A study based on mathematical modeling
The role of interleukin-2 (IL-2) in tumor dynamics is illustrated through mathematical modeling, using delay differential equations with a discrete time delay (a modified version of the Kirshner-Panetta model). Theoretical analysis gives an expression for the discrete time delay and the length of the time delay to preserve stability. Numerical analysis shows that interleukin-2 alone can cause the tumor cell population to regress.
Implementation of the MR tractography visualization kit based on the anisotropic Allen-Cahn equation
Magnetic Resonance Diffusion Tensor Imaging (MR–DTI) is a noninvasive in vivo method capable of examining the structure of human brain, providing information about the position and orientation of the neural tracts. After a short introduction to the principles of MR–DTI, this paper describes the steps of the proposed neural tract visualization technique based on the DTI data. The cornerstone of the algorithm is a texture diffusion procedure modeled mathematically by the problem for the Allen–Cahn...
Improved fuzzy feedback linearization and Sinswat-transformation control of inverted pendulum
Improved results on fuzzy filter design for T-S fuzzy systems.
Improved results on robust stability analysis and stabilization for a class of uncertain nonlinear systems.
Improved robust stability criteria of uncertain neutral systems with mixed delays.
Improving prediction models applied in systems monitoring natural hazards and machinery
A method of combining three analytic techniques including regression rule induction, the k-nearest neighbors method and time series forecasting by means of the ARIMA methodology is presented. A decrease in the forecasting error while solving problems that concern natural hazards and machinery monitoring in coal mines was the main objective of the combined application of these techniques. The M5 algorithm was applied as a basic method of developing prediction models. In spite of an intensive development...
Improving the performance of semiglobal output controllers for nonlinear systems
For a large class of nonlinear control systems, the main drawback of a semiglobal stabilizing output feedback controllers with increasing regions of attraction is that, when the region of attraction is large, the convergence of solutions of the closed-loop system to the origin becomes slow. To improve the performance of a semiglobal controller, we look for a new feedback control law that preserves the semiglobal stability of the nonlinear system under consideration and that is equal to some...