**1. Introduction**

196 Advances in Wavelet Theory and Their Applications in Engineering, Physics and Technology

Zwe-Lee Gaing; Hou-Sheng Huang, 2003. Wavelet-based neural network for power

Zwe-Lee Gaing. 2004. Wavelet-based neural network for power disturbance recognition and

Volume: 3,.13-17 July 2003, Pages: 1628 Vol. 3.

Page(s):1560 – 1568.

disturbance classification. *Power Engineering Society General Meeting*, 2003, IEEE,

classification*. IEEE Transactions on Power Delivery*, Volume 19, Issue 4, Oct. 2004

The aim of the chapter is description of a wavelet transform utilisation in fault diagnosis of analogue electronic circuits. The wavelet transform plays a key role in the presented methods and is located in important step of a feature extraction.

The chapter, among wavelet transform, contains also applications of other modern computational technique: evolutionary optimisation on example of a genetic algorithm, which has proven to be robust and effective optimisation tool for this kind of problems (Bernier et al. 1995; Goldberg, 1989; Grefenstette, 1981, 1986; Holland 1968; De Jong, 1975, 1980; Pettey et al., 1987; Suh & Gucht, 1987; Tanese, 1987).

The author's intention is presentation of a practical utilisation of abovementioned methods (and their combination) in field of testing (fault diagnosis) of analogue electronic circuits.
