Intelligent Target Recognition Based on Wavelet Adaptive Network Based Fuzzy Inference System

Abstract

Abstract. In this paper, an intelligent target recognition system is presented for target recognition from target echo signal of High Resolution Range (HRR) radars. This paper especially deals with combination of the feature extraction and classification from measured real target echo signal waveforms using X -band pulse radar. Because of this, a wavelet adaptive network based fuzzy inference system model developed by us is used. The model consists of two layers: wavelet and adaptive network based fuzzy inference system. The wavelet layer is used for adaptive feature extraction in the time-frequency domain and is composed of wavelet decomposition and wavelet entropy. The used for classification is an adaptive network based fuzzy inference system. The performance of the developed system has been evaluated in noisy radar target echo signals. The test results showed that this system was effective in detecting real radar target echo signals. The correct classification rate was about 93% for used target subjects.

An expert Discrete Wavelet Adaptive Network Based Fuzzy Inference System for digital modulation recognition

“…An algorithm of the hybrid type has been derived for adjusting the parameters of the DWAN-FIS (Davis & Mermelstein, 1980;Tufekci & Gowdy, 2000;Zadeh, 1987). Applications of wavelet adaptive network based fuzzy inference system in the medical field include for detection of electrocardiography changes in patients with partial epilepsy using feature extraction (Bishop, 1996;Wong & Nandi, 2004;Avci, Turkoglu, & Poyraz, 2005b), bearing fault diagnosis based on wavelet transform and fuzzy inference (Avci et al, 2005a;Bishop, 1996), for satellite image fusion (Zadeh, 1987); nevertheless, to date EDWANFIS analysis for automatic digital modulation recognition using adaptive entropy approach is a relatively new approach.…”

A New Expert System for Diagnosis of Lung Cancer: GDA—LS_SVM

“…It is most significant component of designing the expert system [18], [19]. If the features are not chosen well, the best classifier will be poorly.…”

An expert system based on Wavelet Neural Network-Adaptive Norm Entropy for scale invariant texture classification

“…Neural Networks are good at tasks such as pattern matching and classification, function approximation, optimization and data clustering, while traditional computers, because of their architecture, are inefficient at these tasks,especially pattern-matching tasks . As for Wavelet Neural Networks try to combine aspects of the wavelet transformation for purpose of feature extraction and selection with the characteristic decision capabilities of neural network approaches (Avci et al, 2005b). The Wavelet Neural Network (WNN) is constructed based on the wavelet transform theory (Avci et al, 2005c) and is an alternative to feed-forward neural network (Avci et al, 2005c).…”

“…Neural Networks are systems that are constructed to make use of some organizational principles resembling those of the human brain (Avci, Turkoglu, & Poyraz, 2005b). They represent the promising new generation of information processing systems.…”