首页> 外文会议>Advances in Neural Networks - ISNN 2007 pt.2; Lecture Notes in Computer Science; 4492 >Recurrent Fuzzy Neural Network Based System for Battery Charging
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Recurrent Fuzzy Neural Network Based System for Battery Charging

机译:基于递归模糊神经网络的电池充电系统

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Consumer demand for intelligent battery charges is increasing as portable electronic applications continue to grow. Fast charging of battery packs is a problem which is difficult, and often expensive, to solve using conventional techniques. Conventional techniques only perform a linear approximation of a nonlinear behavior of a battery packs. The battery charging is a nonlinear electrochemical dynamic process and there is no exact mathematical model of battery. Better techniques are needed when a higher degree of accuracy and minimum charging time are desired. In this paper we propose soft computing approach based on fuzzy recurrent neural networks (RFNN) training by genetic algorithms to control batteries charging process. This technique does not require mathematical model of battery packs, which are often difficult, if not impossible, to obtain. Nonlinear and uncertain dynamics of the battery pack is modeled by recurrent fuzzy neural network. On base of this FRNN model, the fuzzy control rules of the control system for battery charging is generated. Computational experiments show that the suggested approach gives least charging time and least T_(end)-T_(start) results according to the other intelligent battery charger works.
机译:随着便携式电子应用的持续增长,消费者对智能电池充电的需求正在增长。电池组的快速充电是使用常规技术难以解决且通常昂贵的问题。常规技术仅执行电池组非线性行为的线性近似。电池充电是一个非线性的电化学动态过程,没有确切的电池数学模型。当需要更高的准确性和最短充电时间时,需要更好的技术。本文提出了一种基于遗传算法的模糊递归神经网络(RFNN)训练的软计算方法,以控制电池充电过程。该技术不需要电池组的数学模型,这通常很难甚至不可能获得。电池组的非线性和不确定动力学通过递归模糊神经网络建模。在此FRNN模型的基础上,生成了电池充电控制系统的模糊控制规则。计算实验表明,根据其他智能电池充电器的工作原理,该方法给出的充电时间最短,T_(end)-T_(start)结果最少。

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