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Reliability optimization for FMS (flexible manufacturing system) is very important. Traditional optimal algorithms applying to FMS reliability have many shortcomings. In this article, the authors take into account the characteristics of FMS and bring forward modular neural networks (NN) model based on genetic algorithm (GA) to optimize FMS reliability. Modular NN based on GA has good convergence and short learning time. The example shows that the new algorithm is feasible and effective.

The evaluation of credit guarantee risk (CGR) involves many complex problems such as how to quantify risk, how to express causal relationships of risk factors, how to fuse all kinds of information and evaluate risk with uncertainties. Thus the evaluation of CGR is very difficult because of its uncertainty and diverse information. Bayesian belief networks (BBN) are basically a framework for reasoning uncertainty and diverse information. This paper presents BBN as a new methodology to evaluate CGR. An example is presented by using the BBN method.