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In advanced wireless communication systems that require spectrally efficient modulation schemes, the modulated signal with a high peak-to-average power ratio (PAPR) drives the power amplifier (PA) to operate near the saturation region and introduces serious nonlinearity of the PA. Digital predistortion (DPD) is one of the most promising techniques for PA linearization. In this paper, we propose a...
In this paper, it studies the problems of the on-line identification on the nonlinear and time-lag SISO dynamic system. It puts forward the recurrent structure to linearize the input neurons of the neural network which can describe the feasibility of the algorithm, so the neural network has the dynamic on-line identification capability. Simulation results show that the input linearization dynamic...
The idea of using neural networks in control of dynamic system is comparatively new. The well-known and well-established control methodologies such as classical control (PID) and modern control techniques were developed for control of linear systems. However many practical systems are nonlinear. Nonlinear methods for control do exist but vary from case to case. Neural networks offer a simple and extremely...
The MAV accurate modes are typically unavailable. Most traditional methods for system design are complex and can not get satisfactory effect. Recent work in dynamic inversion with neural network may be applied to control a MAV where the reference commands include position, velocity, attitude and angular rate. This control technology can provide the MAV with an admirably command follow and steady control...
A novel methodology is proposed in this paper for real-time modeling of a nanometer scale positioning stage driven by the piezoelectric ceramics. The precision of the stage is limited by the intrinsic nonlinear and hysteretic behaviors of the actuator. By integrating a second-order linear dynamics and a diagonal recurrent neural network, a nonlinear dynamic model is developed and experimentally validated...
In this paper, the traditional neural network technology has been utilized to modeling the wave-induced ship motions in irregular seas on the Kaohsiung harbor in Taiwan. The training data for neural network is obtained from real case experimental measurements. The proposed tool makes possible the handling of a non-linear dynamic system with insufficient input information. The real case of sea trial...
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