摘 要

在現(xiàn)代機械加工生產過程中，參數(shù)的選擇問題成為困擾設計者的一個難題，它關系到如何提高加工精度并直接影響產品性能及成本，這就使得這一問題也成為設計者積極解決的問題。通過改進人工神經(jīng)網(wǎng)絡的任意非線性映射能力，逼近誤差復映系數(shù)與工件材料、進給量等因素之間的非線性關系，對訓練成熟的網(wǎng)絡輸入加工前毛坯誤差、工件材料硬度等，可以輸出滿足加工要求的加工次數(shù)和各次加工量。
實驗表明：基于人工神經(jīng)網(wǎng)絡的參數(shù)優(yōu)化選擇模型考慮了工件材料、進給量等因素，能夠作出比較準確的預測，它的學習特性能夠以較好的穩(wěn)定性和精度模擬輸入輸出間的非線性關系，有很廣泛的發(fā)展前景。




關鍵詞：參數(shù)優(yōu)化選擇，人工神經(jīng)網(wǎng)絡，BP網(wǎng)絡，MATLAB7.0。



ABSTRACT

In modern production machining process, the parameter optimization is problem puzzling the designer, which is related to how to improve the accuracy of processing and has a direct impact on product performance and cost. This issue attracts designers to find a positive solution.

By improving the arbitrary nonlinear mapping capability of artificial neural networks, the nonlinear connections among the error-reflection, workpiece materials, feed rate and other factors are approximated. The input of the trained network includes rough processing error, the workpiece material hardness and so on; the output includes the processing time and the processing volume, which can meet the processing requirements.
Experiments show that: the optimization model based on artificial neural network, which considers the workpiece material, the feed rate and other factors, can make comparatively accurate forecast. Its characteristics ensure that it can simulate the input-output nonlinear relationship with stability and accuracy, which has a very broad prospect for development.
Key words: parameter optimization, artificial neural network, BP Network，MATLAB7.0.



目 錄
摘要………………………………………………………………………………… Ⅰ
ABSTRACT……………………………………………………………………………Ⅱ
1.緒論……………………………………………………………………………… 1
1.1本文的目的及意義……………………………………………………………………… .1
1.2人工神經(jīng)網(wǎng)絡概率及提出……………………………………………………………… 2
1.3MATLAB的神經(jīng)網(wǎng)絡工具箱…………………………………………………………………3
1.4本文的主要研究內容…………………………………………………………… 4
2.人工神經(jīng)網(wǎng)絡簡介…………………………………………………………… 5
2.1神經(jīng)細胞及人工神經(jīng)元的組成………………………………………………………… 5
2.2人工神經(jīng)網(wǎng)絡的功能…………………………………………………………………… 6
2.3人工神經(jīng)網(wǎng)絡的基本結構……………………………………………………… 7
2.3.1神經(jīng)元網(wǎng)絡的簡化模型…………………………………………………………… 7
2.3.2單層神經(jīng)元網(wǎng)絡和多層神經(jīng)網(wǎng)絡…………………………………………………… 8
2.3.3激活轉移函數(shù)……………………………………………………………… 9
2.4神經(jīng)網(wǎng)絡的發(fā)展方向…………………………………………………………… 11
3. 解決誤差復映的神經(jīng)網(wǎng)絡………………………………………………… 14
3.1誤差復映問題的特點分析……………………………………………………………… 14
3.2神經(jīng)網(wǎng)絡模型的確定…………………………………………………………………… 16
3.3輸入輸出參數(shù)的確定…………………………………………………………… 16
4.BP網(wǎng)絡及算法改進………………………………………………………… 18
4.1BP網(wǎng)絡的結構…………………………………………………………………………… 18
4.2BP網(wǎng)絡存在的問題及算法改進………………………………………………… 18
4.2.1BP網(wǎng)絡存在的問題………………………………………………………………… 18
4.2.2BP算法改進………………………………………………………………… 19
5.數(shù)據(jù)預處理與BP網(wǎng)絡設計………………………………………………… 20
5.1數(shù)據(jù)預處理……………………………………………………………………………… 20
5.2確定網(wǎng)絡的隱層數(shù)和各層神經(jīng)元數(shù)……………………………………………… 20
5.2.1隱層數(shù)的確定………………………………………………………………………… 20
5.2.2隱層神經(jīng)元的選擇…………………………………………………………… 20
5.3訓練參數(shù)及訓練測試…………………………………………………………… 21
5.3.1訓練網(wǎng)絡程序代碼…………………………………………………………………… 21
5.3.2測試網(wǎng)絡及結論………………………………………………………………23
6.圖形界面設計結構……………………………………………………………25
6.1圖形界面設計結果……………………………………………………………………… 25
6.2網(wǎng)絡程序源代碼…………………………………………………………………27
7.總結………………………………………………………………………………30
參考文獻……………………………………………………………………………31
致謝………………………………………………………………………………… 32