基于BP神经网络的智能电缆缆芯温度预测研究

基于BP神经网络的智能电缆缆芯温度预测研究摘要本文针对智能电缆缆温度预测问题，提出了一种基于遗传算法优化BP神经网络的方法。遗传算法作为一种有效的优化算法，其在寻优和搜索方面的优越性已被广泛认可。本研究首先对遗传算法的起源、发展和应用进行了详细的梳理，特别是对其发展过程和应用领域进行了深入的剖析，为后续的研究奠定了基础。遗传算法在常规寻优算法中具有显著的优势，特别是在处理复杂问题和大规模优化问题时表现出较高的效率和准确性。本文进一步对人工神经网络，重点阐述了BP神经网络的理论基础和体系架构。BP神经网络由于其良好的非线性拟合性能，已在许多研究领域得到了很好的结果。但BP神经网络在学习时极易出现局部极值，从而降低了BP网络的预测效果。在此基础上，采用遗传算法对BP神经网络的拓扑和权值进行优化，从而改善了BP神经网络的预测性能。在遗传算法优化BP神经网络的基础上，本文设计了GA-BP优化方案，并实现了相关算法。通过仿真实验，本文对比了用遗传算法直接训练BP网络权值、纯BP算法以及混合GA-BP算法的性能。实验结果表明，混合GA-BP算法在智能电缆缆温度预测问题上的性能优于纯BP算法，验证了本文提出的方法的有效性。总之，在此基础上，将BP神经网络与遗传算法相结合，给出了一种基于BP神经网络的新型电力电缆温度预测的新方式。采用遗传算法对BP神经网络进行了结构及权值的优化选择，从而使BP神经网络的预测效果得到了较好的改善。今后，本文的研究方法和结果可望为智能电缆缆温度预测领域的进一步研究提供参考和借鉴。关键词：神经网络：BP算法：遗传算法AbstractIn this paper,we propose a method to optimize BP neural network based on the problem ofintelligent cable temperature prediction.As an effective optimization algorithm,its advantages inoptimization and search has been widely recognized.This study first combs the origin,development and application of genetic algorithms in detail,especially makes an in-depthanalysis of their development process and application field,which lays a foundation forsubsequent research.Genetic algorithms have significant advantages in conventional optimizationalgorithms,especially showing high efficiency and accuracy when dealing with complexproblems and large-scale optimization problems.This paper further introduces the basic principles and structure of artificial neural network,especially BP neural network.As one of the most widely used artificial neural networks,BPneural network has achieved remarkable results in many fields with its powerful nonlinear fittingability.However,BP neural networks tend to fall into local optima during training,affecting theirprediction performance.To solve this problem,this paper proposes a genetic algorithm-based BPneural network optimization method,which optimizes the structure and connection weights of BPneural network to improve its prediction performance.Based on the genetic algorithm optimization of BP neural network,we design the GA-BPoptimization scheme and implement the related algorithm.Through simulation experiments,thepaper compares the performance of the BP network weights,pure BP algorithm and hybridGA-BP algorithm.The experimental results show that the hybrid GA-BP algorithm outperformsthe pure BP algorithm in the intelligent cable temperature prediction problem,which verifies theeffectiveness of the proposed method.In conclusion,this paper presents a new intelligent cable temperature prediction method bystudying the genetic algorithm and BP neural network.The structure and connection weights ofBP neural network can effectively improve the prediction performance of BP neural network.Inthe future,the research methods and results of this paper are expected to provide reference forfurther research in the field of intelligent cable temperature prediction.Key words:neural network;back-propagation algorithms;genetic algorithms目录摘要Abstract第一章绪论1.1研究背景及意义1.2国内外研究现