... by slow-paced degradation/anomaly. The OANN comprises a complex, fully connected multilayer perceptron, trained offline using nominal, anomaly free data, and remains unchanged during online operation. Its hyperparameters are selected using genetic algorithm-based meta-optimization. The compact NNC...

... convergence and provides excellent quality of final result. Performance of the proposed APSO is compared with those of the real-coded genetic algorithm (GA) and PSO with linearly decreasing inertia weight (LDW-PSO), in terms of the parameter accuracy and convergence speed. Simulation results illustrated...

... , 338 , pp. 615 – 630 . 10.1016/S0016-0032(01)00017-5 Shieh , L. S. , Wang , W. , and Tsai , J. S. H. , 1999 , “ Optimal Digital Design of Hybrid Uncertain Systems Using Genetic Algorithms ,” IEE Proc.: Control Theory Appl. 1350-2379 , 146 ( 2 ), pp. 119 – 130 . 10.1049/ip...

... pattern, is given as a training signal. In this paper, the operation results of the system obtained via genetic algorithm (GA) were used as the training signal for the neural network. Operation planning (the amount of hydrogen production and the amount of exhaust heat storage) of the system on arbitrary...

...Qian Wang, Graduate Student; Robert F. Stengel, Professor A method of designing a family of robust compensators for a single-input/single-output linear system is presented. Each compensator’s transfer function is found by using a genetic-algorithm search for numerator and denominator coefficients...