Yanghui Hu , Silu Yan , Hongliang Lu , Lin Cheng , Dongyu Zhang , Ranran Zhao , Yuming Zhang
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引用次数: 0
Abstract
A genetic algorithm (GA) optimized back propagation (BP) neural network based power amplifier (PA) modeling method is proposed, which employs the boundary-equidistant sampling method for dividing the training data and validation data in the case of multidimensional data. The differences in modeling accuracy before and after optimization of the BP neural network are compared, and the differences in modeling accuracy under different sampling strategies are compared. The results show that the BP neural network optimized based on GA has higher modeling accuracy. The borderless sampling method proposed in this paper not only ensures the uniformity of the sampled data, but also covers the critical data at the border.
期刊介绍:
Published since 1969, the Microelectronics Journal is an international forum for the dissemination of research and applications of microelectronic systems, circuits, and emerging technologies. Papers published in the Microelectronics Journal have undergone peer review to ensure originality, relevance, and timeliness. The journal thus provides a worldwide, regular, and comprehensive update on microelectronic circuits and systems.
The Microelectronics Journal invites papers describing significant research and applications in all of the areas listed below. Comprehensive review/survey papers covering recent developments will also be considered. The Microelectronics Journal covers circuits and systems. This topic includes but is not limited to: Analog, digital, mixed, and RF circuits and related design methodologies; Logic, architectural, and system level synthesis; Testing, design for testability, built-in self-test; Area, power, and thermal analysis and design; Mixed-domain simulation and design; Embedded systems; Non-von Neumann computing and related technologies and circuits; Design and test of high complexity systems integration; SoC, NoC, SIP, and NIP design and test; 3-D integration design and analysis; Emerging device technologies and circuits, such as FinFETs, SETs, spintronics, SFQ, MTJ, etc.
Application aspects such as signal and image processing including circuits for cryptography, sensors, and actuators including sensor networks, reliability and quality issues, and economic models are also welcome.