Spectrum sensing is a fundamental requirement for cognitive radio, and it is a challenging problem in impulsive noise modeled by symmetric alpha-stable (SαS) distributions. The Gaussian kernelized energy detector (GKED) performs better than the conventional detectors in SαS distributed noise. However, it fails to detect the DC signal and has high computational complexity. To solve these problems, this paper proposes a more efficient and robust detector based on a Gaussian function (GF). The analytical expressions of the detection and false alarm probabilities are derived and the best parameter for the statistic is calculated. Theoretical analysis and simulation results show that the proposed GF detector has much lower computational complexity than the GKED method, and it can successfully detect the DC signal. In addition, the GF detector performs better than the conventional counterparts including the GKED detector in SαS distributed noise with different characteristic exponents. Finally, we discuss the reason why the GF detector outperforms the conventional counterparts.

A hybrid evolutionary neuro-fuzzy system (HENFS) is proposed in this paper, where the weighted Gaussian function (WGF) is used as the membership function for improved premise construction. With the WGF, different types of the membership functions (MFs) can be accommodated in the rule base of HENFS. A new hybrid algorithm of random optimization (RO) algorithm incorporated with the least square estimation (LSE) is presented. Based on the hybridization of RO-LSE, the proposed soft-computing approach overcomes the disadvantages of other widely used algorithms. The proposed HENFS is applied to chaos time series identification and industrial process modeling to verify its feasibility. Through the illustrations and comparisons the impressive performances for unknown system identification can be observed.

A new method is developed to generate fuzzy rules from numerical data. This method consists of two algorithms: Algorithm 1 is used to identify structures of the given data set, that is, the optimal number of rules of system; Algorithm 2 is used to identify parameter of the used model. The former is belonged to unsupervised learning, and the latter is belonged to supervised learning. To identify parameters of fuzzy model, we developed a neural network which is referred to as Unsymmetrical Gaussian Function Network (UGFN). Unlike traditional fuzzy modelling methods, in the present method, a) the optimal number of rules (clusters) is determinde by input-output data pairs rather than by only output data as in sugeno's method, b) parameter identification of ghe present model is based on a like-RBF network rather than backpropagation algorithm. Our method is simple and effective because it integrates fuzzy logic with neural networks from basic network principles to neural architecture, thereby establishing an unifying framework for different fuzzy modelling methods such as one with cluster analysis or neural networks and so on.