人工蜂群优化的BP神经网络提升系统识别与控制

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"基于人工蜂群的BP神经网络是一种创新性的研究，由Feihu Jin和Guang Shu在哈尔滨工业大学计算科学与技术系提出。他们的工作聚焦于将人工蜂群算法与传统的反向传播（BackPropagation，BP）神经网络相结合，以解决传统BP神经网络存在的问题。BP神经网络因其广泛应用于诸如系统识别、控制系统等领域而受到重视，但由于其缺点，如搜索精度低、收敛速度慢以及容易陷入局部最优解，限制了其性能。人工蜂群算法作为一种模拟进化策略，具有正反馈、分布式计算和建设性贪心收敛的优点。它借鉴蜜蜂的社会行为，通过群体协作寻找解决方案，具有全局搜索能力强和快速收敛的特点。将人工蜂群算法融入BP神经网络，旨在创建一个在线学习模型，提高系统的实时性和稳定性。在控制领域，例如对倒立摆系统的控制中，这种融合可以显著提升模型的泛化能力和控制器的性能。研究者通过实验展示了人工蜂群算法与BP神经网络结合后的显著效果，即提升了神经网络的扩展映射能力，同时加快了算法在全局优化上的搜索速度。关键词包括：人工蜂群、神经网络、系统识别、倒立摆系统。这项工作对于改进现有神经网络模型的优化策略和实际应用有着重要的理论和实践价值，为人工智能领域的研究提供了新的视角和方法。"

Back Propagation Neural Network

Based on Artificial Bee Colony Algorithm

Feihu Jin, Guang Shu

Department of Computing and Science, Harbin University of Science and Technology, Harbin China

fhjin88@163.com

Abstract—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.

Keywords; artificial bee colony; neural network; system

identification; inverted pendulum system

I. INTRODUCTION

The research on neural network is very active and has been

made considerable progress in recent years. Neural network

has complex nonlinear mapping ability, function

approximation and large-scale parallel distributed computing

ability. The most common application of neural network is

multilayer feed forward neural network model. Among them,

back propagation(BP) algorithm is widely used because of its

rigorous and high universality. Because the BP learning

algorithm is used with gradient descent algorithm, training

usually takes a long time to converge, and will inevitably

encounter the local minimum problem.

Artificial bee colony(ABC) algorithm is a novel simulated

evolutionary algorithm. The algorithm was firstly proposed by

Turkish scholar Karaboga[1]. ABC was successfully applied to

some practical problems, such as unconstrained numerical

optimization[2,3], constrained numerical optimization[4,5],

digital filter design[6], aircraft attitude control[7], and made a

series of good experimental results.

Artificial bee colony algorithm and neural network are

combined in this paper. The algorithm has the extensive

mapping ability of neural network and global convergence and

fast heuristic learning characteristics of artificial bee colony

algorithm, avoiding the slow convergence speed of neural

network and easy to fall into local minimum. This paper uses

artificial bee colony algorithm to learn the weights of neural

network, combining the two aspects to solve the nonlinear

model identification and the control of inverted pendulum.

II. T

HE PRINCIPLE OF ARTIFICIAL BEE COLONY

ALGORITHM

Bee colony can always easily found good nectar source.

German biologist Frisch found that the bees dance to

communicate the information of nectar source[8]. Employee

bee return beehive and dancing; the bees crawl along a straight

line, and then turn left, moving as figure eight and swinging

their belly, which is called the waggle dance. The angle

between the gravity direction and the centre axis of the dance is

exactly equal to the angle between the sun and food source.

The intelligent behavior of the dance consists of 3 basic parts:

employee bees, onlooker bees EF, scout bees UF. Three

behavior patterns are defined: the search for nectar, honey

recruitment and abandoning nectar.

dance area A

dance area B

nectar

source A

nectar

source B

hive

unloading

nectar A

unloading

nectar B

potetial

EF1

EF2 UFEF1

EF2

EF1

EF2

EF1

Figure 1. The behaviour of honey bee searching for nectar

(1)Nectar source. Nectar represents the various possible

solutions: the nectar value depends on a variety of factors, such

as distance between honey and honeycomb, the amount of

concentration energy and the difficult level of getting energy.

Use the digital quantity "benefit" to measure the nectar source

characteristics.

(2)Onlooker Bees EF. Onlooker bees are contacted with

nectar which is they are working. Bees carry the specific source

information. The information includes the distance of nectar

source and honeycomb, the direction, the benefit of nectar.

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