基于神经网络微分方程的智能捕鱼多重混沌发生器

Y. Itou, T. Tomono, M. Minami, A. Yanou
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引用次数: 3

摘要

对几条鱼进行连续的抓放实验,使鱼找到了在池角静止不动等逃跑策略。为了使捕鱼机器人的智能超过鱼的适应和逃离机器人手上的追捕网的能力,我们认为需要一些超越鱼的适应智能的东西。在这里,我们提出了一个由神经网络微分方程(NNDE)组成的混沌发生器和一个进化机制,使NNDE产生尽可能多的混沌轨迹。我们认为,由于网的不可预测的混沌运动可能超出了鱼对网运动的适应能力,因此具有许多不同混沌的混沌运动的鱼不可能有足够的适应性来逃离追逐网。本文从理论上介绍了可产生多种混沌的NNDE混沌生成系统,并结合李雅普诺夫数、庞加莱返回映射和初值灵敏度对混沌进行了分析。
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Multiple chaos generator by Neural-Network-Differential-Equation for intelligent fish-catching
Continuous catching and releasing experiment of several fishes makes the fishes find some escaping strategies such as staying stationary at corner of the pool. To make fish-catching robot intelligent more than fishes' adapting and escaping abilities from chasing net attached at robot's hand, we thought something that goes beyond the fishes' adapting intelligence will be required. Here we propose a chaos-generator comprising Neural-Network-Differential-Equation(NNDE) and an evolving mechanism to have the NNDE generate chaotic trajectories as many as possible. We believe that the fish could not be adaptive enough to escape from chasing net with chaos motions that have many different chaos, since unpredictable chaotic motions of net may go beyond the fishes' adapting abilities to the net motions. In this report we introduce the chaos generating system by NNDE, which can produce many kinds of chaos theoretically, and then analyze the chaos with Lyapunov number, Poincare return map and initial value sensitivity.
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