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Chun-Sheng Cheng

Publications and source records attributed to Chun-Sheng Cheng.

2 recordsLinked to original sources
PubMed2004

Epidemiology of peptic ulcer disease in Wuhan area of China from 1997 to 2002.

AIM: To describe the epidemiological features of peptic ulcer disease in Wuhan area during 1997-2002, to analyze the sex, age and occupation characteristics, as well as the geographic distribution of peptic ulcer disease, and to determine the effective methods of preventing and controlling peptic ulcer disease. METHODS: In the early 1980s, the peptic ulcer disease registry system was established to collect the data of peptic ulcer disease in Wuhan area. Here we performed a statistically detailed analysis of 4876 cases of peptic ulcer disease during 1997-2002. RESULTS: The morbidity of peptic ulcer disease between males and females was significantly different (chi(2) = 337.9, P<0.001). The majority of peptic ulcer diseases were found at the age of 20 to 50 years. Because of different occupations, the incidence of peptic ulcer disease was different in different areas. CONCLUSION: The incidence of peptic ulcer disease is highly associated with sex, age, occupation and geographic environmental factors. By analyzing the epidemiological features of peptic ulcer disease, we can provide the scientific data for prevention and control of peptic ulcer disease.

Adolescent↗
PubMed2004

Dynamical optimal training for interval type-2 fuzzy neural network (T2FNN).

Type-2 fuzzy logic system (FLS) cascaded with neural network, type-2 fuzzy neural network (T2FNN), is presented in this paper to handle uncertainty with dynamical optimal learning. A T2FNN consists of a type-2 fuzzy linguistic process as the antecedent part, and the two-layer interval neural network as the consequent part. A general T2FNN is computational-intensive due to the complexity of type 2 to type 1 reduction. Therefore, the interval T2FNN is adopted in this paper to simplify the computational process. The dynamical optimal training algorithm for the two-layer consequent part of interval T2FNN is first developed. The stable and optimal left and right learning rates for the interval neural network, in the sense of maximum error reduction, can be derived for each iteration in the training process (back propagation). It can also be shown both learning rates cannot be both negative. Further, due to variation of the initial MF parameters, i.e., the spread level of uncertain means or deviations of interval Gaussian MFs, the performance of back propagation training process may be affected. To achieve better total performance, a genetic algorithm (GA) is designed to search optimal spread rate for uncertain means and optimal learning for the antecedent part. Several examples are fully illustrated. Excellent results are obtained for the truck backing-up control and the identification of nonlinear system, which yield more improved performance than those using type-1 FNN.

Algorithms↗