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Generating chaos with neural-network-differential-equation for intelligent fish-catching robot

机译:用神经网络微分方程生成混沌智能捕鱼机器人

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摘要

We have been investigating a method to compose some intelligent robot. Continuous catching and releasing experiment of several fishes induces the fishes to find some escaping strategies such as staying stationarily at corners of the pool. To make fish-catching robot intelligent more than fish's adapting and escaping abilities, we have proposed a chaos-generator comprising Neural-Network-Differential-Equation(NNDE) and an evolving mechanism to have the system 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 since unpredictable chaotic motions of net may go beyond the fish's adapting abilities to the net motions. In this report we confirmed that the proposed system can generate plural chaos by examining chaotic characters of chaos trajectories generated by NNDE through Lyapunov number, Poincare return map, initial value sensitivity, fractal dimension and bifurcation map.
机译:我们一直在研究一种构成智能机器人的方法。连续捕捞和释放多种鱼类的实验使鱼类找到了一些逃避策略,例如固定地呆在游泳池的角落。为了使抓鱼机器人比鱼的适应和逃避能力更智能,我们提出了一种包括神经网络微分方程(NNDE)和进化机制的混沌发生器,以使系统尽可能多地产生混沌轨迹。我们认为,鱼的适应能力不足以逃脱带有混沌运动的网,因为无法预测的网混沌运动可能超出了鱼对网运动的适应能力。在本报告中,我们证实了该系统可以通过检查LDEpunov数,庞加莱返回图,初始值敏感度,分形维数和分叉图来检查NNDE生成的混沌轨迹的混沌特性,从而产生多个混沌。

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