Artificial Mains Network

For measuring EMI, we need to use an ISN ( Impedance Stabilization Network ). In off-line power supplies, this becomes a LISN (Line Impedance Stabilization Network) — also called an AMN (Artificial Mains Network). See Figure 9-2 for a simplified schematic. Note that the LISN, as recommended for CISPR-22 compliance, is detailed in CISPR 16. 

Such applications of NN as a predictive method make the artificial neural networks another technique of data treatment, comparable to parametric empirical modeling by, for example, numerical regression methods [e.g., 10,11] briefly mentioned in section 16.1. The main advantage of NN is that the network needs not be programmed because it learns from sets of experimental data, which results in the possibility of representing even the most complex implicit functions, and also in better modeling without prescribing a functional form of the actual relationship. Another field of... 

A generalised structure of an electronic nose is shown in Fig. 15.9. The sensor array may be QMB, conducting polymer, MOS or MS-based sensors. The data generated by each sensor are processed by a pattern-recognition algorithm and the results are then analysed. The ability to characterise complex mixtures without the need to identify and quantify individual components is one of the main advantages of such an approach. The pattern-recognition methods maybe divided into non-supervised (e.g. principal component analysis, PCA) and supervised (artificial neural network, ANN) methods also a combination of both can be used. 

Main influencing factors of water inrush were confirmed as the distance between working-face with fault, thickness of overburden rocks, water levels of aquifer IIII and V. Aimed at the inflow of water in workface, the early warning model was established based on the Artificial Neural Network, and was checked up with the observational data of water inrush in Zhuxianzhuang coal mine. 

A currently popular approach to classification and pattern-recognition problems involves neural networks. Neural networks are mainly used as (non-)linear approximations to multivariable functions or as classifiers (Ripley, 1993). Principally, the technique is intended to mimic the computational properties of the brain, which is highly parallel in its operation. Artificial neural networks (Figure 3.10) consist of units with some of the properties of real neurons. 

Basically, four main areas of methods for gear fault detection have been published. Signal processing techniques. Statistical analysis (ANDRADE, ESAT, BADI, 2001 BAYDAR eta/., 2001 TUMER HUFF, 2003 HE, KONG, YAN, 2007), Time-series analysis (ZHAN JARDINE, 2005 ZHAN, MAKIS, JAR-DINE, 2006) and Artificial neural networks AYA ESAT, 1997 SAMANTA, 2004 SANZ, PERERA, HUERTA, 2007 RAFIEE et cd., 2007). 

The application of artificial neural networks (ANN) to the mix proportion design, using the least paste content and a mathematical model to express the relation between main variables (wic ratio, fly ash/binder ratio, aggregate grain distribution) was proposed by many authors, for example, Ji et al. (2006) who declared better mechanical and economical properties of obtained concretes. 

The main reason for the utility of artificial neural networks (ANN) for modeling purposes is related to the incomplete knowledge of the mechanisms of polymerization processes and to the fact that the available phenomenological models are being developed and solved with difficulty or do not provide accurate results. For these types of processes, designing neural networks represents modeling alternatives for providing predictions useful for experimental practice. 

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