基于蚁群优化算法的BP神经网络的RPROP混合算法仿真的研究

Research on RPROP hybrid algorithm model of BP neural network based on ant colony optimization algorithm

摘要:

本文对蚁群优化算法的BP神经网络中的RPROP混合算法进行了研究,提出了利用蚁群优化算法,结合RPROP混合算法解决无线网络传感器中如何处理信息服务点中大量的冗余数据、网络运行速度等相关问题,通过建立系统构架及信息服务点,证明该算法能够延长BP神经网络的生命周期,加快BP神经网络的收缩速度,能够将网络中信息服务点的重复数据进行有效的合并处理,并及时过滤掉非正常信息服务点的数据,减少数据服务点的能量消耗,期训练过程中迭代次数改善明显,解决BP神经网络的学习、训练时间冗余等问题,同时具有较强的计算、寻优等能力,提高了网络分类正确率和运行的效率,是一种较为实用的算法,完全能够满足日益增长的无线互联网终端的运行需要。

Abstract:

This paper studied the RPROP hybrid BP neural network algorithm and ant colony optimization algorithm for the proposed using ant colony optimization algorithm to solve a large number of redundant data to information service point of the wireless sensor networks in RPROP hybrid algorithm, the related issues of network speed, through the establishment of system architecture and information service. to prove that the algorithm BP neural network can prolong the life cycle. accelerate the contraction rate of BP neural network, will be able to repeat information service in the network are combined effectively, and timely to filter the non normal information service point data, reduce the energy consumption of data service, the iterative training process period times improved. solve BP neural network learning, training time redundancy. and has strong ability of calculation, optimization, improve the accuracy of network classification and operation efficiency Rate is a more practical algorithm. which can fully meet the growing needs of wireless internet terminal operation.