![](https://static.zsdocx.com/FlexPaper/FileRoot/2019-3/11/10/37df243e-2aef-43e4-8ad3-c7159f4a3009/37df243e-2aef-43e4-8ad3-c7159f4a3009pic.jpg)
![基于人工神經(jīng)網(wǎng)絡的工具消耗量預測研究.pdf_第1頁](https://static.zsdocx.com/FlexPaper/FileRoot/2019-3/11/10/37df243e-2aef-43e4-8ad3-c7159f4a3009/37df243e-2aef-43e4-8ad3-c7159f4a30091.gif)
版權說明:本文檔由用戶提供并上傳,收益歸屬內(nèi)容提供方,若內(nèi)容存在侵權,請進行舉報或認領
文檔簡介
1、河北工業(yè)大學碩士學位論文基于人工神經(jīng)網(wǎng)絡的工具消耗量預測研究姓名:王雅琪申請學位級別:碩士專業(yè):管理科學與工程指導教師:高迎平20090401基于人工神經(jīng)網(wǎng)絡的工具消耗量預測研究 ii RESEARCH ON THE TOOLS CONSUMPTION FORECASTING BASED ON THE ARTIFICIAL NEURAL NETWORK ABSTRACT Rational use and consumption of t
2、ools place importantly, which in production management, occupation of liquidity, cost and product quality. The preparations of traditional fixed consumptions are by the fixed members in factories, and the calculation is
3、manual. These works are time-consuming and laborious to enterprises, which are multi-species production. These enterprises can not meet the market competition. The Artificial Neural Network model of tools consumption is
4、constructed in this thesis. Draw on traditional methods, the model have six input variables. These variables are the number of processed products, tool life, cutting speed, tool wear rate of accidents, production volume,
5、 and technical level of workers. Based on the construction method of Artificial Neural Network, the BP (error back-propagation) and RBF (radial basis function) Artificial Neural Network model of tools consumption are con
6、structed. Samples was trained and tested in Data Processing System, which was collected from tools section of ZMJ. In addition, use the traditional method of technical calculations to calculate tools consumption is menti
7、oned in this thesis. Prediction accuracy of Artificial Neural Network model higher than traditional method is proved in this thesis, by comparison the forecast results of technical calculations and Artificial Neural Netw
溫馨提示
- 1. 本站所有資源如無特殊說明,都需要本地電腦安裝OFFICE2007和PDF閱讀器。圖紙軟件為CAD,CAXA,PROE,UG,SolidWorks等.壓縮文件請下載最新的WinRAR軟件解壓。
- 2. 本站的文檔不包含任何第三方提供的附件圖紙等,如果需要附件,請聯(lián)系上傳者。文件的所有權益歸上傳用戶所有。
- 3. 本站RAR壓縮包中若帶圖紙,網(wǎng)頁內(nèi)容里面會有圖紙預覽,若沒有圖紙預覽就沒有圖紙。
- 4. 未經(jīng)權益所有人同意不得將文件中的內(nèi)容挪作商業(yè)或盈利用途。
- 5. 眾賞文庫僅提供信息存儲空間,僅對用戶上傳內(nèi)容的表現(xiàn)方式做保護處理,對用戶上傳分享的文檔內(nèi)容本身不做任何修改或編輯,并不能對任何下載內(nèi)容負責。
- 6. 下載文件中如有侵權或不適當內(nèi)容,請與我們聯(lián)系,我們立即糾正。
- 7. 本站不保證下載資源的準確性、安全性和完整性, 同時也不承擔用戶因使用這些下載資源對自己和他人造成任何形式的傷害或損失。
最新文檔
- 基于人工神經(jīng)網(wǎng)絡的油氣資源量預測方法研究.pdf
- 基于人工神經(jīng)網(wǎng)絡的事故預測研究.pdf
- 基于人工神經(jīng)網(wǎng)絡的經(jīng)濟預測研究.pdf
- 基于人工神經(jīng)網(wǎng)絡的徑流預測研究.pdf
- 基于人工神經(jīng)網(wǎng)絡的冰情預測研究.pdf
- 基于人工神經(jīng)網(wǎng)絡的輪軌力預測.pdf
- 定額消耗量
- 基于人工神經(jīng)網(wǎng)絡的商品銷售量預測的應用與研究.pdf
- 基于人工神經(jīng)網(wǎng)絡的預測研究的文獻綜述
- 基于人工神經(jīng)網(wǎng)絡的沉降預測方法研究.pdf
- 基于人工神經(jīng)網(wǎng)絡的路基沉降預測研究.pdf
- 基于人工神經(jīng)網(wǎng)絡的短期負荷預測.pdf
- 基于人工神經(jīng)網(wǎng)絡的HPC強度預測.pdf
- 基于灰關聯(lián)與人工神經(jīng)網(wǎng)絡的混凝土中鋼筋銹蝕量預測.pdf
- 基于人工神經(jīng)網(wǎng)絡的電網(wǎng)日負荷預測研究.pdf
- 基于BP人工神經(jīng)網(wǎng)絡的短期交通預測研究.pdf
- 基于人工神經(jīng)網(wǎng)絡的軟件質(zhì)量預測模型研究.pdf
- 石油消耗量降低
- 基于人工神經(jīng)網(wǎng)絡的混凝土強度預測模型.pdf
- 基于灰色預測和人工神經(jīng)網(wǎng)絡組合的負荷預測.pdf
評論
0/150
提交評論