›› 2008, Vol. 25 ›› Issue (2): 224-232.DOI: 10.7523/j.issn.2095-6134.2008.2.013
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ＸＩＡＮＧＷｅｉＳＨＥＮＧＪｉｅＣＨＥＮＺｏｎｇＨａｉ
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Abstract: ＴｈｅＨａｍｍｅｒｓｔｅｉｎｍｏｄｅｌｉｓｃｏｍｐｏｓｅｄｏｆａｎｏｎｌｉｎｅａｒｓｔａｔｉｃｅｌｅｍｅｎｔａｎｄａｌｉｎｅａｒｄｙｎａｍｉｃｅｌｅｍｅｎｔｓｅｒｉａｌｌｙ，ａｎｄｉｔｐｒｏｖｅｓｔｏｂｅｅｆｆｅｃｔｉｖｅｉｎｄｅｓｃｒｉｂｉｎｇｔｈｅｂｅｈａｖｉｏｒｏｆｍａｎｙｃｈｅｍｉｃａｌｐｒｏｃｅｓｓｅｓ．Ｂｙａｐｐｒｏｐｒｉａｔｅｉｄｅｎｔｉｆｉｃａｔｉｏｎ，ｔｈｅｉｎｔｒｉｃａｔｅｎｏｎｌｉｎｅａｒｃｏｎｔｒｏｌｐｒｏｂｌｅｍｏｆｔｈｉｓｍｏｄｅｌｃａｎｂｅｆａｃｉｌｉｔａｔｅｄｉｎｔｏｔｗｏｐｒｏｂｌｅｍｓ：ｔｈｅｃｏｎｔｒｏｌｏｆｔｈｅｌｉｎｅａｒｐａｒｔａｎｄｔｈｅｓｏｌｕｔｉｏｎｏｆｔｈｅｎｏｎｌｉｎｅａｒｐａｒｔ．Ｉｎｔｈｉｓｐａｐｅｒ，ａｍｏｄｅｌｐｒｅｄｉｃｔｉｖｅｃｏｎｔｒｏｌｓｃｈｅｍｅｉｓｐｒｏｐｏｓｅｄ，ｗｈｉｃｈｕｓｅｓａｓｅｔｏｆｎｅｕｒａｌｎｅｔｗｏｒｋｓｔｏａｐｐｒｏｘｉｍａｔｅｔｈｅｉｎｖｅｒｓｅ ｍａｐｐｉｎｇｏｆｔｈｅｎｏｎｌｉｎｅａｒｂｌｏｃｋ．Ｔｈｉｓｎｅｕｒａｌｎｅｔｗｏｒｋｓｍｅｔｈｏｄｎｅｅｄｎｔａｓｓｕｍｅｔｈａｔｔｈｅｎｏｎｌｉｎｅａｒｂｌｏｃｋｉｓ ａｐｏｌｙｎｏｍｉａｌｅｑｕａｔｉｏｎ，ｔｈｕｓｉｔｏｖｅｒｃｏｍｅｓｔｈｅｄｉｆｆｉｃｕｌｔｙｔｈａｔｎｏｒｅａｌｒｏｏｔｓｅｘｉｓｔｆｏｒｔｈｅｐｏｌｙｎｏｍｉａｌｅｑｕａｔｉｏｎ．Ｔｗｏｓｉｍｕｌａｔｉｏｎｅｘａｍｐｌｅｓ，ｉｎｃｌｕｄｉｎｇａｐＨｎｅｕｔｒａｌｉｚａｔｉｏｎｐｒｏｃｅｓｓ，ａｒｅｕｓｅｄｔｏｄｅｍｏｎｓｔｒａｔｅｔｈｅｅｆｆｅｃｔｉｖｅｎｅｓｓ ｏｆｔｈｅｍｅｔｈｏｄ．
Key words: ｍｏｄｅｌｐｒｅｄｉｃｔｉｖｅｃｏｎｔｒｏｌ, Ｈａｍｍｅｒｓｔｅｉｎｍｏｄｅｌ, ｎｅｕｒａｌｎｅｔｗｏｒｋｓ
CLC Number:
TP13
ＸＩＡＮＧＷｅｉＳＨＥＮＧＪｉｅＣＨＥＮＺｏｎｇＨａｉ. ＭｏｄｅｌｐｒｅｄｉｃｔｉｖｅｃｏｎｔｒｏｌｂａｓｅｄｏｎｎｅｕｒａｌｎｅｔｗｏｒｋｓｆｏｒＨａｍｍｅｒｓｔｅｉｎｔｙｐｅｎｏｎｌｉｎｅａｒｓｙｓｔｅｍｓ[J]. , 2008, 25(2): 224-232.
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URL: http://journal.ucas.ac.cn/EN/10.7523/j.issn.2095-6134.2008.2.013
http://journal.ucas.ac.cn/EN/Y2008/V25/I2/224
