Research Article
Recursive Identification for Fractional Order Hammerstein Model Based on ADELS
| (1) | Define the objective function ; | | (2) | Initialize parameters of the Chebyshev map: m and ; | | (3) | Initialize individuals ; | | (4) | Evaluate all the individuals in the population by the objective function ; | | (5) | Initialize the number of iteration k = 1; | | (6) | While (k < max number of iterations N) | | (7) | For each individual | | (8) | Update operators adaptively (equations (13)–(15)); | | (9) | The mutation vector is obtained by mutation (equations (16)); | | (10) | If the generated mutation vector exceeds the boundary, a new mutation vector is generated randomly, until it is within the boundary; | | (11) | The trial vectors is obtained by equations (7); | | (12) | The best individuals is obtained by greedy selection (equations (8)); | | (13) | Find the current best according to the local search strategy (equations (17)–(19)); | | (14) | If a new optimal individual is obtained, it will be randomly copied to ten individuals of the next generation population; | | (15) | End | | (16) | k = k + 1; | | (17) | End while; | | (18) | Postprocess results and visualization. |
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