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12 Table 3 Constraints of support-pairs with rotative movement Constraints Values are hidden in weights via Eqs. (3) and (4). x p 0 ¼ 21:0; W0j ¼ u ð3Þ o p 0 ¼ 21:0; W0k ¼ u ð4Þ The global error function as: E ¼ 1 2N X N X L p¼1 k¼1 d p k 2 y p k 2 Weight-adjusted formula as: DW ¼ 2hð›E=›WÞ Eq. (5) shows the calculating model of the adjusting weight in the output layer (h represents the learning ratio and a represents the momentum coefficient). s p k DW p jk ¼ y p k 1 2 y p k p p ¼ hsk oj DW jk ¼ h XN p¼1 s p p k oj d p k 2 y p k W jkðt þ 1Þ ¼W jkðtÞþDW jkðtÞþaDW jkðt 2 1Þ The formula of adjusting weights of the hidden layer is shown as Eq. (6) s p i DW p ij ¼ o p j 1 2 o p j p ¼ hsj x;p i DW ij ¼ h XN p¼1 s p p j xi X L k¼1 1 0.5 0 Lubrication (LU) Need Any None Speed (SP) Low Middle High Load (LD) Low Middle High Temperature (TP) Low General High Precision (PR) Low General High Structure (ST) None General Compact s p k W jk Fig. 5. The BP networks for searching support-parts knowledge. S. Zhou et al. / Knowledge-Based Systems 16 (2003) 7–15 ð5Þ ð6Þ Fig. 6. Input interface of constraints on Support-Parts with Rotative Movement in IPDP. W ijðt þ 1Þ ¼W ijðtÞþDW ijðtÞþaDW ijðt 2 1Þ In BP neural networks, the coefficients h and a should be greater than zero and less than one. The groups of training samples of BP neural networks should be three to ten times the number of weights [14]. 5.3. An example of knowledge search As seen earlier, the mathematical model of the ANN is stated. Here, how to apply the ANN model for searching and retrieving knowledge from the repositories of the IPDP is discussed. The illustration is based on rotative support parts stated in Section 5.1. Corresponding to the knowledge function support-parts with rotative movement shown in Fig. 4, the constraints are shown in Table 3. The knowledge matched with the function requirement of the rotative support is the rolling bearing, the journal bearing and the magnetic bearing. Here, setting the number of hidden nodes of BP networks at 4, the structure of Fig. 7. Output interface of result on Support-Parts with Rotative Movement in IPDP.
the BP network is shown as Fig. 5. The input parameters of the BP networks are the constraints shown in Table 3: their values are endued with 1, 0.5 or 0. If the output value of BP networks is greater than 0.8, it should be set at 1; if it is less than 0.2, it should be set at 0. Output values (1,0,0), (0,1,0) and (0,0,1) represent the rolling bearing, journal bearing and magnetic bearing, respectively. Combining the ANN and ASP technique, and migrating the trained ANN program to the Internet environment, the IPDP achieves the knowledge search via Internet control based on a function driven concept. Once a user wants to retrieve the knowledge needed from the repositories of the IPDP, he only needs to log into the servers of the IPDP and confine the function of knowledge and the constraints that the knowledge should meet (e.g. lubrication conditions needs, any or none; working load heavy, general or slight; working speed high, middle or low; etc.). Then the IPDP system will return the results, which coincide with the requirements to the user. The input interface of ‘support parts with rotative movement’ shows as Fig. 6, the output interface is shown as Fig. 7. 6. Prototype of the IPDP The prototype of the IPDP is introduced in this section; its structure is stated as Section 2. The client side of the prototype system adopts Solidworks and VCþþ programs (design platform) used for the structure design and performance analysis of products. The browser for the client side employs a general WWW browser (e.g. MS Internet Explorer, Netscape, etc). The pre/post processor of the prototype, which translates data between STEP information models and design platforms, employs STEP Developer 7.0, developed by STEP Tools Inc. The repositories of the server side are built with the SQL server database. The servers of the IPDP achieve communication with the database through ADO and ODBC. The servers were constructed upon a Peer Web Server in a Windows NT4.0 workstation environment. The structure diagram of the prototype system is shown as Fig. 8. Fig. 9 shows a piece of knowledge searched from certain common repository of S. Zhou et al. / Knowledge-Based Systems 16 (2003) 7–15 13 Fig. 8. The prototype of Internet-based intensive product design platform. Fig. 9. A piece of searched knowledge (rolling bearing) from repository of IPDP. IPDP. Fig. 10 indicates that importing the searched knowledge into design platform (SolidWorks) to aid the structure design of rotor-bearing system. The knowledge is represented in standard and neutral form, and its store and transfer are achieved successfully over the Internet, but these are only the precondition to the implementation of the IPDP servicing for product design. Ultimately, the knowledge retrieved from the repositories of Fig. 10. The structure design of rotor system using searched knowledge.
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Page 3 and 4: Table 1 EXPRESS information model o
Page 5: Fig. 4. The flowchart of searching
Page 9: Acknowledgments This research was p

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