基于人工蜂群算法的反向传播神经网络

Feihu Jin, Guang Shu
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引用次数: 9

摘要

人工蜂群算法是一种新颖的模拟进化算法。人工蜂群算法具有正反馈、分布式计算和建设性贪婪启发式收敛的特点。反向传播是一种广泛应用于许多领域的前馈神经网络,但它存在解精度低、搜索速度慢、容易收敛到局部极小值等缺点。采用人工蜂群算法和反向传播神经网络相结合的方法,实现了非线性模型的辨识和倒立摆的控制。仿真结果表明,将人工蜂群算法与神经网络相结合可以获得神经网络的广泛映射能力和人工蜂群算法的快速全局收敛性。
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Back propagation neural network based on artificial bee colony algorithm
The artificial bee colony algorithm is a novel simulated evolutionary algorithm. The artificial bee colony algorithm has positive feedback, distributed computation and a constructive greedy heuristic convergence. Back propagation is a kind of feed forward neural network widely used in many areas, but it has some shortcomings, such as low precision solutions, slow search speed and easy convergence to the local minimum. The combination of artificial bee colony algorithm and back propagation neural network is adopted so that a nonlinear model can be identified and an inverted pendulum can be controlled. Simulation results show that the extensive mapping ability of neural network and the rapid global convergence of artificial bee colony algorithm can be obtained by combining artificial bee colony algorithm and neural network.
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