Identification of immune models for fault detection

Mitchell

International Journal of Systems Science

et al. 2008

206

142

Section: Model Construction Using Stepwise Selection Algorithmsmentioning

confidence: 99%

Model selection approaches for non-linear system identification: a review

Mitchell

International Journal of Systems Science

et al. 2008

206

142

“…The OFR algorithm has been a popular tool in associative neural networks such as fuzzy/neurofuzzy systems [8], [9] and wavelets neural networks [10], [11]. The algorithm has also been utilized in a wide range of engineering applications, e.g., aircraft gas turbine modeling [12], fuzzy control of multipleinput-multiple-output nonlinear systems [13], power system control [14], and fault detection [15]. In optimum experimental design [16], D-optimality criterion is regarded as most effective in optimizing the parameter efficiency and model robustness via the maximization of the determinant of the design matrix.…”

Section: Introductionmentioning

confidence: 99%

A New RBF Neural Network With Boundary Value Constraints

IEEE Trans. Syst., Man, Cybern. B

2009

Abstract-We present a novel topology of the radial basis function (RBF) neural network, referred to as the boundary value constraints (BVC)-RBF, which is able to automatically satisfy a set of BVC. Unlike most existing neural networks whereby the model is identified via learning from observational data only, the proposed BVC-RBF offers a generic framework by taking into account both the deterministic prior knowledge and the stochastic data in an intelligent manner. Like a conventional RBF, the proposed BVC-RBF has a linear-in-the-parameter structure, such that it is advantageous that many of the existing algorithms for linear-in-theparameters models are directly applicable. The BVC satisfaction properties of the proposed BVC-RBF are discussed. Finally, numerical examples based on the combined D-optimality-based orthogonal least squares algorithm are utilized to illustrate the performance of the proposed BVC-RBF for completeness.Index Terms-Boundary value constraints (BVC), D-optimality, forward regression, radial basis function (RBF), system identification.

show abstract

“…For the linear-in-the-parameters models, the forward orthogonal least squares (OLS) algorithm efficiently constructs parsimonious models [6], [7], and has been a popular tool in associative neural networks such as fuzzy/neurofuzzy systems [8], [9] and wavelet neural networks [10], [11]. The algorithm has also been utilized in a wide range of engineering applications, e.g., aircraft gas turbine modeling [12], fuzzy control of multiple-input-multiple-output (MIMO) nonlinear systems [13], power system control [14], and fault detection [15].…”

Section: Introductionmentioning

confidence: 99%

“…Consensus problems often arise in the applications of multiagent systems [1]- [4] and have received much attention in recent years. There is a large amount of papers concerning such problems (see [5]- [15], [17], [19] and references therein).…”

Section: Introductionmentioning

confidence: 99%

“…One of the important classes of dynamically changing network topologies is the so-called "switching topoldogy," where the network topology switches at a sequence of time points, randomly or controlled by a given rule. Consensus problems with switching topologies have been addressed in several papers such as [7], [9], [15], [17], [19], and others.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A-Optimality Orthogonal Forward Regression Algorithm Using Branch and Bound

IEEE Trans. Neural Netw.

Harris

2008