A radial basis function neural network optimized through modified DIRECT algorithm based-model for a three interconnected water tank

In many physical systems, it is difficult to obtain a model structure that is highly nonlinear and complex. However, models are usually linear, but not suitable in such form to model processes because they contain a significant number of simplifying hypotheses which are insufficient for the design of reliable controllers. The absence of robustness with respect to system parameters does not ensure the performance specifications of the control system knowing that the nominal parametric state rarely corresponds to the real one. For these raisons, it is beneficial to use a specific technique to characterize accurately system dynamics in an entirely uncertain environment. In this work, we present an approach to approximate and validate over a large operating range the dynamic behaviour of a Three Tank System benchmark based on a radial basis function neural network (RBFNN). The proposed RBFNN is applied to solve the parametric-identification problems for nonlinear and complex system by using a modified DIRECT algorithm to search the network parameters. The learning algorithm is developed by combining the DIRECT algorithm and a linear regression for fast convergence. Different experimental results have been performed to show the effectiveness of the RBFNN model to emulate the dynamic behaviour of the nonlinear and complex system under different situation

A new method for automatic defects detection and diagnosis in rolling element bearings using Wald test

To detect and to diagnose, the localized defect in rolling bearings, a statistical model based on the sequential Wald test is established to generate a “hypothetical” signal which takes the state zero in absence of the defect, and the state one if a peak of resonance caused by the defect in the bearing is present. The autocorrelation of this signal allows one to reveal the periodicity of the defect and, consequently, one can establish the diagnosis by comparing the frequency of the defect with the characteristic frequencies of the bearing. The originality of this work is the use of the Wald test in the signal processing domain. Secondly, this method permits the detection without considering the level of noise and the number of observations, it can be used as a support for the Fast Fourier Transform. Finally, the simulated and experimental signals are used to show the efficiency of this method based on the Wald test

Derivative-free global ship design optimization using global/local hybridization of the DIRECT algorithm

The application of global/local hybrid DIRECT algorithms to the sim- ulation-based hull form optimization of a military vessel is presented, aimed at the reduction of the resistance in calm water. The specific features of the black-box-type objective function make the problem suitable for the application of DIRECT-type algorithms. The objective function is given by numerical iterative procedures, which could lead to inaccurate derivative calculations. In addition, the presence of local minima cannot be excluded a priori. The algorithms proposed (namely DIRMIN and DIRMIN-2) are hybridizations of the classic DIRECT algorithm, with deterministic derivative-free local searches. The algorithms’ performances are first assessed on a set of test problems, and then applied to the ship optimization application. The numerical results show that the local hybridization of the DIRECT algorithm has beneficial effects on the overall computational cost and on the efficiency of the simulation-based optimization procedure