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Description: 一个能够将windows的中文字体转换成Vxworks中C语言源代码的工具，中文字体无删除线。在其他uCOS等嵌入式系统中也应该都能用。-one of the windows can be converted into Chinese fonts Vxworks C language source code tools, Chinese fonts without deleted. In other uCOS other embedded system can also be used.
Platform: |
Size: 270146 |
Author: 王振华 |
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Description: 一个能够将windows的中文字体转换成Vxworks中C语言源代码的工具，中文字体无删除线。在其他uCOS等嵌入式系统中也应该都能用。-one of the windows can be converted into Chinese fonts Vxworks C language source code tools, Chinese fonts without deleted. In other uCOS other embedded system can also be used.
Platform: |
Size: 270336 |
Author: Nick |
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Description: system gen & accel dsp 培训资料-system gen & accel dsp
Platform: |
Size: 7614464 |
Author: ocean |
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Description: Introduction
Setting up the System Generator
Tool
A Quick Tour of the System
Generator
System Generator Basic Tutorial-Introduction Setting up the System Generator Tool A Quick Tour of the System Generator System Generator Basic Tutorial
Platform: |
Size: 575488 |
Author: bobor |
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Description: System Generator 多媒体处理算法实现。包含很多实例,是一个提高教程。-System Generator multimedia processing algorithms. Contains many examples, is an enhanced tutorial.
Platform: |
Size: 1826816 |
Author: hucy |
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Description: The proposed approach is based on three stages which (1) use neural networks for constructing a response
function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating
overall performance of a specific factor combination, and (3) use a genetic algorithm to optimize
parameter design. Effectiveness of the proposed approach is illustrated with a simulated example. Analysis
results reveal that the approach has higher performance than the traditional experimental design.
Platform: |
Size: 325632 |
Author: heddam salim |
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Description: The proposed approach is based on three stages which (1) use neural networks for constructing a response
function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating
overall performance of a specific factor combination, and (3) use a genetic algorithm to optimize
parameter design. Effectiveness of the proposed approach is illustrated with a simulated example. Analysis
results reveal that the approach has higher performance than the traditional experimental design.
Platform: |
Size: 359424 |
Author: heddam salim |
Hits:
Description: The proposed approach is based on three stages which (1) use neural networks for constructing a response
function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating
overall performance of a specific factor combination, and (3) use a genetic algorithm to optimize
parameter design. Effectiveness of the proposed approach is illustrated with a simulated example. Analysis
results reveal that the approach has higher performance than the traditional experimental design.
Platform: |
Size: 331776 |
Author: heddam salim |
Hits:
Description: The proposed approach is based on three stages which (1) use neural networks for constructing a response
function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating
overall performance of a specific factor combination, and (3) use a genetic algorithm to optimize
parameter design. Effectiveness of the proposed approach is illustrated with a simulated example. Analysis
results reveal that the approach has higher performance than the traditional experimental design.
Platform: |
Size: 359424 |
Author: heddam salim |
Hits:
Description: The proposed approach is based on three stages which (1) use neural networks for constructing a response
function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating
overall performance of a specific factor combination, and (3) use a genetic algorithm to optimize
parameter design. Effectiveness of the proposed approach is illustrated with a simulated example. Analysis
results reveal that the approach has higher performance than the traditional experimental design.
Platform: |
Size: 356352 |
Author: heddam salim |
Hits:
Description: The proposed approach is based on three stages which (1) use neural networks for constructing a response
function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating
overall performance of a specific factor combination, and (3) use a genetic algorithm to optimize
parameter design. Effectiveness of the proposed approach is illustrated with a simulated example. Analysis
results reveal that the approach has higher performance than the traditional experimental design.
Platform: |
Size: 257024 |
Author: heddam salim |
Hits:
Description: The proposed approach is based on three stages which (1) use neural networks for constructing a response
function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating
overall performance of a specific factor combination, and (3) use a genetic algorithm to optimize
parameter design. Effectiveness of the proposed approach is illustrated with a simulated example. Analysis
results reveal that the approach has higher performance than the traditional experimental design.
Platform: |
Size: 237568 |
Author: heddam salim |
Hits:
Description: The proposed approach is based on three stages which (1) use neural networks for constructing a response
function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating
overall performance of a specific factor combination, and (3) use a genetic algorithm to optimize
parameter design. Effectiveness of the proposed approach is illustrated with a simulated example. Analysis
results reveal that the approach has higher performance than the traditional experimental design.
Platform: |
Size: 279552 |
Author: heddam salim |
Hits:
Description: The proposed approach is based on three stages which (1) use neural networks for constructing a response
function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating
overall performance of a specific factor combination, and (3) use a genetic algorithm to optimize
parameter design. Effectiveness of the proposed approach is illustrated with a simulated example. Analysis
results reveal that the approach has higher performance than the traditional experimental design.
Platform: |
Size: 278528 |
Author: heddam salim |
Hits:
Description: The proposed approach is based on three stages which (1) use neural networks for constructing a response
function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating
overall performance of a specific factor combination, and (3) use a genetic algorithm to optimize
parameter design. Effectiveness of the proposed approach is illustrated with a simulated example. Analysis
results reveal that the approach has higher performance than the traditional experimental design.
Platform: |
Size: 244736 |
Author: heddam salim |
Hits:
Description: The proposed approach is based on three stages which (1) use neural networks for constructing a response
function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating
overall performance of a specific factor combination, and (3) use a genetic algorithm to optimize
parameter design. Effectiveness of the proposed approach is illustrated with a simulated example. Analysis
results reveal that the approach has higher performance than the traditional experimental design.
Platform: |
Size: 361472 |
Author: heddam salim |
Hits:
Description: The proposed approach is based on three stages which (1) use neural networks for constructing a response
function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating
overall performance of a specific factor combination, and (3) use a genetic algorithm to optimize
parameter design. Effectiveness of the proposed approach is illustrated with a simulated example. Analysis
results reveal that the approach has higher performance than the traditional experimental design.
Platform: |
Size: 344064 |
Author: heddam salim |
Hits:
Description: The proposed approach is based on three stages which (1) use neural networks for constructing a response
function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating
overall performance of a specific factor combination, and (3) use a genetic algorithm to optimize
parameter design. Effectiveness of the proposed approach is illustrated with a simulated example. Analysis
results reveal that the approach has higher performance than the traditional experimental design.
Platform: |
Size: 349184 |
Author: heddam salim |
Hits:
Description: The proposed approach is based on three stages which (1) use neural networks for constructing a response
function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating
overall performance of a specific factor combination, and (3) use a genetic algorithm to optimize
parameter design. Effectiveness of the proposed approach is illustrated with a simulated example. Analysis
results reveal that the approach has higher performance than the traditional experimental design.
Platform: |
Size: 212992 |
Author: heddam salim |
Hits:
Description: The proposed approach is based on three stages which (1) use neural networks for constructing a response
function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating
overall performance of a specific factor combination, and (3) use a genetic algorithm to optimize
parameter design. Effectiveness of the proposed approach is illustrated with a simulated example. Analysis
results reveal that the approach has higher performance than the traditional experimental design.
Platform: |
Size: 295936 |
Author: heddam salim |
Hits: