非线性自回归神经网络

  • V7_954630
    了解作者
  • 4.7KB
    文件大小
  • zip
    文件格式
  • 0
    收藏次数
  • VIP专享
    资源类型
  • 0
    下载次数
  • 2022-04-08 02:05
    上传日期
神经网络,非线性
NARX-Neural-Network-Hyper-Parameter-Tuning-And-Training--Matlab-main.zip
  • NARX-Neural-Network-Hyper-Parameter-Tuning-And-Training--Matlab-main
  • HyperParameterTuning.m
    1.8KB
  • trainLoop.m
    4.8KB
  • README.md
    3.2KB
内容介绍
# NARX-Neural-Network-Hyper-Parameter-Tuning-And-Training--Matlab This work includes the hyper parameter tuning of a NARX neural network in Matlab. This is used to determine the ideal number of delays in both the inputs and outputs, just as the number of neurons in the hidden layer. The flowchart below describes this process. <img src="https://user-images.githubusercontent.com/40301612/96038758-87535700-0e5f-11eb-9243-aa4bfd7c9d02.png" width="500"> The process requires the input of the iteration limits: number of trials, numTrials, maximum and minimum number of hidden layer neurons, Hmax and Hmin respectively, and maximum memory depth, Mmax. In order to minimize the bad start problem, present in the Levenberg-Marquardt algorithm, the process starts by iterating over a set number of trials, numT rials, with a fixed memory depth and number of neurons in the hidden layer. At each iteration of this inner loop, the initial parameter solution is randomized and subsequently optimized by the minimization algorithm. This procedure is carried out with the network in an open loop, or series-parallel configuration, meaning the delayed outputs fed into the network are real values taken from the extraction signals. For extraction of the network’s parameters, the data set is first divided into three contiguous parts, where the first 70% of the data is used to update the weight’s and biases, 15% for online validation whose error is monitored during extraction and 15% for model validation. When the number of trials reaches its maximum, the structure’s accuracy is calculated in terms of the mean value of validation NMSEs across the trials. This process is then repeated for all combinations of memory depth and number of hidden layer neurons. The described procedure was realized with numT rials, Hmin, Hmax and Mmax set to 20, 5, 20 and 10 respectively. The results are shown in a 3D plot. <img src="https://user-images.githubusercontent.com/40301612/96039042-faf56400-0e5f-11eb-9f58-d70020f2ccdd.png" width="500"> By analysing figure it was decided that a memory depth of 4 with 10 neurons in the hidden layer would be a good compromise between complexity and accuracy. The optimal structure was trained in a single loop akin to the inner loop of the previous procedure. The overall procedure is described by the flowchart below. The difference is that, at each iteration, after doing the open loop extraction with randomized initial solutions, the network is converted into closed loop or parallel configuration, meaning its previous predictions are used as inputs. ![paraExtraction (3)](https://user-images.githubusercontent.com/40301612/96039037-f92ba080-0e5f-11eb-8e2d-ad5b14bec51c.png) The network’s performance is then evaluated on both validation and extraction data in terms of its NMSE. The end result is the weights and biases that led to the smallest overall NMSE. During the extraction, the number of trials was set to 100 while a regularization performance ratio of 0.3, set by trial and error, was used in order to diminish the variance of the solutions. Open and closed loop results are shown below ![extractionResults (2)](https://user-images.githubusercontent.com/40301612/96040126-b23eaa80-0e61-11eb-84a3-aadaf2d442f0.png)
评论
    相关推荐