Recently, many researchers have used nature inspired metaheuristic algorithms due to their ability to perform optimally on complex problems. To solve problems in a simple way, in the recent era bat algorithm has become famous due to its high tendency towards convergence to the global optimum most of the time. But, still the standard bat with random walk has a problem of getting stuck in local minima. In order to solve this problem, this research proposed bat algorithm with levy flight random walk. Then, the proposed Bat with Levy flight algorithm is further hybridized with three different variants of ANN. The proposed BatLFBP is applied to the problem of insulin DNA sequence classification of healthy homosapien. For classification performance, the proposed models such as Bat levy flight Artificial Neural Network (BatLFANN) and Bat levy Flight Back Propagation (BatLFBP) are compared with the other state-of-the-art algorithms like Bat Artificial Neural Network (BatANN), Bat back propagation (BatBP), Bat Gaussian distribution Artificial Neural Network (BatGDANN). And Bat Gaussian distribution back propagation (BatGDBP), in-terms of means squared error (MSE) and accuracy. From the perspective of simulations results, it is show that the proposed BatLFANN achieved 99.88153% accuracy with MSE of 0.001185, and BatLFBP achieved 99.834185 accuracy with MSE of 0.001658 on WL5. While on WL10 the proposed BatLFANN achieved 99.89899% accuracy with MSE of 0.00101, and BatLFBP achieved 99.84473% accuracy with MSE of 0.004553. Similarly, on WL15 the proposed BatLFANN achieved 99.82853% accuracy with MSE of 0.001715, and BatLFBP achieved 99.3262% accuracy with MSE of 0.006738 which achieve better accuracy as compared to the other hybrid models.
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共查询到17条相似文献，搜索用时 265 毫秒
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介绍基于FPGA的判决反馈均衡器设计,均衡器中采样LMS算法;在使用相同算法的前提下,比较线性均衡器和判决反馈均衡器的性能及不同抽头系数的判决反馈均衡器的性能,用VHDL语言实现判决反馈均衡器,并在Active HDL仿真软件中进行仿真。 相似文献
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为构建用于某语音信号传输系统盲均衡器的BP神经网络模型,编写了基于BP神经网络的盲均衡算法伪代码,计算了算法的时间复杂度,分析了BP神经网络输入层神经元个数、隐含层神经元个数和隐含层层数对盲均衡算法性能的影响,评估了基于Sigmoid的变步长算法、基于误差补偿的变步长算法和基于误差的变步长算法对基于BP神经网络的盲均衡器性能的改进效率,据此设计了一种含双隐层结构的BP神经网络盲均衡器,并对其性能进行了数值仿真分析,明确了其适用范围,为该语音信号传输系统设计提供了技术支撑。 相似文献
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阐述了自适应格型判决反馈均衡器,研究了它在水声通信中的应用,仿真比较了最小均方判决反馈均衡器(LMSDFE)、递归最小二乘判决反馈均衡器(RLSDFE)和最小二乘格型判决反馈均衡器(LSLDFE),并给出最小二乘格型判决反馈均衡器(LSLDFE)在湖试中的结果。 相似文献
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基于人工神经网络的油库安全综合评价方法 总被引：7，自引：0，他引：7
油库安全是一个涉及多因素、多层次结构、复杂的非线性系统。在分析影响油库安全状况因素的基础上,将人工神经网络理论应用于油库安全综合评价之中,建立了油库综合评价的多层次结构神经网络模型,并介绍实现的方法及其优点。评价实例证明该方法评价效果显著。 相似文献
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目前对京津冀地区Q235钢的腐蚀地图未见报道。应用误差反向传播(BP)神经网络,创建了Q235大气腐蚀预测模型,并实现了京津冀地区的腐蚀预测。基于地理信息系统(GIS)绘制了京津冀地区碳钢大气腐蚀速率、腐蚀等级和腐蚀等级概率地图。结果表明:京津冀地区Q235大气腐蚀等级为二级、三级和四级,大气腐蚀速率、腐蚀等级及达到各腐蚀等级的概率在空间上分布基本相同,呈现出东南高西北低、沿海高内陆低的趋势。唐山和邢台的Q235大气腐蚀速率和达到高腐蚀等级概率最大,此现象与该地区大气中污染物SO2和NO2含量相对较高相关。 相似文献
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转动架作为某型游乐设备的关键部件,其固有频率和屈曲强度直接关系着结构的稳定性。运用有限元软件ANSYS Workbench建立转动架的有限元模型,通过模态分析和谐响应分析得到对转动架振动影响最为显著的固有频率,通过屈曲稳定性分析得到转动架失稳时的屈曲特征值,并运用试验设计获得140个样本点。为探索各个设计参数对转动架固有频率和屈曲强度的灵敏度,运用MATLAB软件建立BP（back propagation,反向传播）神经网络数学模型并对样本点进行拟合,再通过Isight与MATLAB的集成应用,利用描述性蒙特卡洛抽样法对神经网络模型进行数值模拟。结果表明：对转动架稳定性影响较大的危险模态主要是第10至第12阶模态;对转动架危险模态频率影响较大的主要是肢杆壁厚度、肢杆截面长度和肢杆截面宽度,而对转动架屈曲稳定性影响较为明显的则是肢杆、腹杆和圆板的6个尺寸参数。研究结果表明,合理地改善对转动架稳定性影响较大的设计参数将有效地提升其稳定性和设计效率,这可为该结构的后续设计和优化提供一定的借鉴和参考。 相似文献
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针对浅海水声信道长时延、强多途干扰的特点,建立了一种新的带自适应循环判决反馈均衡的Turbo译码结构,并对其性能进行了仿真研究.该结构对典型的Turbo译码器进行了改进,使之不但能输出信息比特,同时又能输出校验比特,这些比特经硬判决、符号映射及信道交织后反馈回判决反馈均衡器,构成带自适应循环判决反馈均衡的Turbo译码结构.厦门港实测5途浅海水声信道仿真结果显示,该结构具有优良的抗多途性能,信噪比大于4dB时,误比特率小于10-5,比联合判决反馈均衡的Turbo译码结构提高了约2.5dB. 相似文献
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在水声通信领域多途引起的码间干拢可以用均衡消减。盲均衡不需要训练序列,这将有效的节省通信带宽,提高通信效率及通信性能。实际中的通信信道不可能是完全线性的,神经网络作为一种非线性动态系统,具有大规模并行处理及高度的鲁棒性特征,将其应用于水声信道盲均衡切实可行。文中对变步长BP算法的前馈神经网络进行了理论和算法分析,并通过计算机对其实现水声信道盲均衡进行了仿真。仿真结果表明采用变步长BP算法比采用传统BP算法的神经网络盲均衡器收敛速度快,均衡性能明显提高。 相似文献
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Muhammad Zubair Rehman Muhammad Aamir Nazri Mohd. Nawi Abdullah Khan Saima Anwar Lashari Siyab Khan 《计算机、材料和连续体（英文）》2022,73(1):493-511
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基于神经网络的海洋平台损伤诊断研究 总被引：5，自引：3，他引：5
提出了基于神经网络技术的海洋平台分类多重损伤诊断方法，此方法先将海洋平台结构的构件进行分类和分层，将各类构件损伤引起的标准化的频率变化率输入BP网络，建立系统，进行损伤构件所属的类和所在的层的判定；然后将损伤层内某类构件损伤引起的标准化的损伤信号指标输入概率神经网络，建立系统，进行损伤构件具体位置的判定；最后将损伤构件损伤引起的频率平方的变化比输入BP网络，建立系统，进行损伤程度诊断。数值仿真结果表明。此方法可完成对海洋平台结构的损伤诊断，且具有一定的抗干扰能力。 相似文献
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Xiangyan Tang Dengfang Feng KeQiu Li Jingxin Liu Jinyang Song Victor S. Sheng 《计算机、材料和连续体（英文）》2023,74(2):2789-2802
Data prediction can improve the science of decision-making by making predictions about what happens in daily life based on natural law trends. Back propagation (BP) neural network is a widely used prediction method. To reduce its probability of falling into local optimum and improve the prediction accuracy, we propose an improved BP neural network prediction method based on a multi-strategy sparrow search algorithm (MSSA). The weights and thresholds of the BP neural network are optimized using the sparrow search algorithm (SSA). Three strategies are designed to improve the SSA to enhance its optimization-seeking ability, leading to the MSSA-BP prediction model. The MSSA algorithm was tested with nine different types of benchmark functions to verify the optimization performance of the algorithm. Two different datasets were selected for comparison experiments on three groups of models. Under the same conditions, the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of the prediction results of MSSA-BP were significantly reduced, and the convergence speed was significantly improved. MSSA-BP can effectively improve the prediction accuracy and has certain application value. 相似文献
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