ABSTRACT
This chapter demonstrates the M-ary frequency shift keying (MFSK) signals and mathematical expressions. It explains the feature extraction using discrete wavelet transform (DWT) and high-order moments. The chapter presents a general review of adaptive neural-fuzzy inference system (ANFIS). It focuses on the methodology and analysis of simulation. Frequency modulation is the process when the baseband signal modulates the carrier frequency of the modulated signal. If the baseband signals are digital signals, then the process is called frequency shift keying (FSK). Wavelet transform (WT) analyzes the signals into different frequency components. A fuzzy inference system (FIS) is a process of using the theory of fuzzy sets and fuzzy rules to map a given input to an output. The performance of FIS depends on the identification of membership functions (MFs) and the fuzzy rules tuned to the requested applications. There is degradation in performance of artificial neural network (ANN) classifiers at high signal-to-noise ratio (SNR).
