The presented work addresses application of the evidence procedure to the field of signal processing where ill-posed estimation problems are frequently encountered. We base our analysis on the Relevance Vector Machines (RVM) technique originally proposed by M. Tipping. It effectively locally maximizes the evidence integral for linear kernel-based models. We extend the RVM technique by consideri...

A broad class of nonlinear systems and filters can be modeled by the Volterra series representation. However, its practical use in nonlinear system identification is sometimes limited due to the large number of parameters associated with the Volterra filter’s structure. The parametric complexity also complicates design procedures based upon such a model. This limitation for system identificatio...

To improve our understanding of nonlinear elastic properties of soils, a method is proposed of estimation of stress-strain relations of soils in situ in strong ground motion based on vertical array data. Strong motion records provided by seismic vertical arrays allow estimation of nonlinear stressstrain relations in soil layers at different depths, from the surface down to the location of the d...

By transforming identification and control for nonlinear system into optimization problems, a novel optimization method named state transition algorithm (STA) is introduced to solve the problems. In the proposed STA, a solution to a optimization problem is considered as a state, and the updating of a solution equates to a state transition, which makes it easy to understand and convenient to imp...

This paper presents a nonparametric identification of continuous-time nonlinear systems by using a Gaussian process (GP) model. The GP prior model is trained by artificial bee colony algorithm. The nonlinear function of the objective system is estimated as the predictive mean function of the GP, and the confidence measure of the estimated nonlinear function is given by the predictive covariance...

The method of structural identification nonlinear dynamic systems is offered in the conditions of uncertainty. The method of construction the set containing the data about a nonlinear part of system is developed. The concept of identifiability system for a solution of a problem structural identification is introduced. The special class of structures S for a solution of problem identification is...

Dynamic neural networks are often used for nonlinear system identification. This paper presents a novel series-parallel dynamic neural network structure which is suitable for nonlinear system identification. A theoretical proof is given showing that this type of dynamic neural network is able to approximate finite trajectories of nonlinear dynamical systems. Also, this neural network is trained...

Nonlinear system identification using recurrent neural network with genetic algorithm is presented. A continuous-time model of Hopfield neural network is used in this study. Its convergence properties are first evaluated. Then the model is implemented to identify nonlinear systems. Recurrent network‘s operational factors of the system identification scheme are obtained by genetic algorithm. Mat...

The model identification of the nonlinear system has been concerned by the industrial community all along. The relationship of the nonlinear dynamic system is contained in the data accumulated in the scene. To better utilize the data about the industrial objects, in this article, we put forward the nonlinear system predictor driven by the Bayesian-Gaussian neural network (NN) model, use the tra...