This paper deals with minimum spanning tree problems where each edge weight is a fuzzy random variable. In order to consider the imprecise nature of the decision maker's judgment, a fuzzy goal for the objective function is introduced. A novel decision making model is constructed based on possibility theory and on a stochastic programming model. It is shown that the problem including both randomness and fuzziness is reduced to a deterministic equivalent problem. Finally, a polynomial-time algorithm is provided to solve the problem.

In the present paper, we focus ourselves on the turning point (TP) algorithm proposed by Mueller and evaluate its performance when applied to a Gaussian signal with definite covariance function. Then the ECG wave is modeled by Gaussian signals: namely, the ECG is divided into two segments, the baseline segment and the QRS segment. The baseline segment is modeled by a Gaussian signal with butterworth spectrum and the QRS one by a narrow-band Gaussian signal. Performance of the TP algorithm is evaluated and compared when it is applied to a real ECG signal and its Gaussian model. The compression rate (CR) and the normalized mean square error (NMSE) are used as measures of performance. These measures show good coincidence with each other when applied to Gaussian signals with the mentioned spectra. Our results suggest that performance evaluation of the compression algorithms based on the stochastic-process model of ECG waves may be effective.