Yajun Zhang

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Clustering in the neural-network literature is generally based on the competitive learning paradigm. The paper addresses two major issues associated with conventional competitive learning, namely, sensitivity to initialization and difficulty in determining the number of prototypes. In general, selecting the appropriate number of prototypes is a difficult(More)
For a complex industrial system, its multivariable and nonlinear nature generally make it very difficult, if not impossible, to obtain an accurate model, especially when the model structure is unknown. The control of this class of complex systems is difficult to handle by the traditional controller designs around their operating points. This paper, however,(More)
This paper presents a novel nonlinear control strategy for a class of uncertain single-input and single-output discrete-time nonlinear systems with unstable zero-dynamics. The proposed method combines adaptive-network-based fuzzy inference system (ANFIS) with multiple models, where a linear robust controller, an ANFIS-based nonlinear controller and a(More)
In this paper, we investigate the performance of time division broadcast protocol (TDBC) with incremental relaying (IR) when there are multiple available relays. Opportunistic relaying (OR), i.e., the " best " relay is select for transmission to minimize the system's outage probability , is proposed. Two OR schemes are presented. The first scheme, termed(More)
Acute gastroenteritis caused by human noroviruses (NoVs) has become an important public health problem worldwide. This study was carried out to investigate the rates of NoV infections and the genetic characteristics of NoVs in adult outpatients with acute gastroenteritis in Ji’nan, a large eastern city in China. A total of 480 fecal samples were collected(More)
—Data quantization methods for continuous attributes play an extremely important role in artificial intelligence, data mining and machine learning because discrete values of attributes are required in most classification methods. In this paper, we present a supervised statistical data quantization method. It defines a quantization criterion based on the(More)