Dynamic System Identification Using Adaptive Algorithm
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Over the latest few years the part of System Identification has drawn interest of numerous researchers due to its wide applicability to different fields. Adaptive direct modeling or system identification and adaptive inverse modeling or channel equalization find extensive applications in telecommunication, control system, instrumentation, power system engineering and geophysics. The identification task becomes very difficult, if the plants or systems are nonlinear and dynamic in nature. Further, the existing conventional methods like the least mean square (LMS) algorithms do not provide suitab...
Over the latest few years the part of System Identification has drawn interest of numerous researchers due to its wide applicability to different fields. Adaptive direct modeling or system identification and adaptive inverse modeling or channel equalization find extensive applications in telecommunication, control system, instrumentation, power system engineering and geophysics. The identification task becomes very difficult, if the plants or systems are nonlinear and dynamic in nature. Further, the existing conventional methods like the least mean square (LMS) algorithms do not provide suitable training to build up precise direct and inverse models. Very often these (LMS) derivative based algorithms do not lead to optimal solutions in pole-zero and Hammerstein type system identification problem as they have tendency to be trapped by local minima. To overcome this problem, in this book the Genetic algorithm (GA), Bacterial Foraging Optimization (BFO) and differential evolution (DE) technique has been properly applied to develop a latest model for efficient identification of nonlinear dynamic system.
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