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Description: 汽车PKE + IMMO DEMO板源程序, 能过LF线圈,对 转发器的读取, 加密, 写入功能. 并可实现PKE的间易功能. 想研究汽车无钥匙系统的欢迎交流.
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Size: 69735 |
Author: exucong@126.com |
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Description: 本文详细解析了奔驰E280无钥进入系统(PEPS)的结构组成和工作原理-Resolved in detail the structure and working principle of the Mercedes-Benz E280 Keyless Entry System (PEPS
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Size: 265216 |
Author: sandywu |
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Description: PEPS控制系统源码，用UCOSII，一键启动控制系统，PKE加密-PEPS control system source code, with UCOSII, a key to start the control system, PKE encryption
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Size: 23552 |
Author: 冯健 |
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Description: The cement industry is one of the most important and profitable industries in Iran and great
content of financial resources are investing in this sector yearly. In this paper a GMDH-type
neural network and genetic algorithm is developed for stock price prediction of cement sector.
For stocks price prediction by GMDH type-neural network, we are using earnings per share
(EPS), Prediction Earnings Per Share (PEPS), Dividend per share (DPS), Price-earnings ratio
(P/E), Earnings-price ratio (E/P) as input data and stock price as output data. For this work, data
of ten cement companies is gathering Tehran stock exchange (TSE) in decennial range
(1999-2008). GMDH type neural network is designed by 80 of the experimental data. For
testing the appropriateness of the modeling, reminder of primary data were entered into the
GMDH network. The results are very encouraging and congruent with the experimental results.-The cement industry is one of the most important and profitable industries in Iran and great
content of financial resources are investing in this sector yearly. In this paper a GMDH-type
neural network and genetic algorithm is developed for stock price prediction of cement sector.
For stocks price prediction by GMDH type-neural network, we are using earnings per share
(EPS), Prediction Earnings Per Share (PEPS), Dividend per share (DPS), Price-earnings ratio
(P/E), Earnings-price ratio (E/P) as input data and stock price as output data. For this work, data
of ten cement companies is gathering Tehran stock exchange (TSE) in decennial range
(1999-2008). GMDH type neural network is designed by 80 of the experimental data. For
testing the appropriateness of the modeling, reminder of primary data were entered into the
GMDH network. The results are very encouraging and congruent with the experimental results.
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Size: 452608 |
Author: mohammad |
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