Fuzzy Stochastic Simulation

For an uncertain function U X — (Ui X), U2 X)), Ui X) and U2(X) both involve the expected value of fuzzy random variable dg t). We will simulate 

Step 2 Randomly withdraw sample coin from sample space H according to the probability distribution Pr  

Step 4 Repeat from Step 2 to Step 3 for N times  

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Then we simulate the uncertain function U X (Ui(X), UziX)) to get the input and output data by fuzzy stochastic simulation Third, we approach the uncertain function U(X) using three-layer feedforward neural networks Finally, we embed the well trained neural network into the generic algorithms to get the hybrid intelligent algorithm. The details are to be discussed as follows. 

Generate the input and output data for uncertain function using fuzzy stochastic simulation (training samples). 

Pc = 0.6, mutation probability pm = 0.5, iteration times Gmax = 10,000, number of times for fuzzy stochastic simulation is 6000, with 3000 training samples. Figure 6.6 illustrates the convergence process of the objective function. The algorithm finds the optimum value of objective function at generation... 

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