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[Other resource] ProSOM

Description: SOM的学习规则有三个主要阶段： 1）寻找与输入模式xk最接近的连接权向量Wj*=（wj*1, wj*2,….., wj*N） 2）将该连接权向量Wj*进一步朝向与输入模式xk接近的方向调整 3）除调整连接权向量Wj*外，还调整邻域内的各个连接权向量，并随着学习次数的增加，逐渐缩小邻域范围-SOM learning rules are three main stages : 1) Find the input mode xk closest to the connection weights vector Wj *= (wj * 1, * 2 wj ... .., wj * N) 2) for the right to connect Vector further towards * Wj with input mode xk close reorientation 3) Subject to adjustment weights Vector * Wj, also adjusted neighborhood in all the right connections Vector, and with the increase in the number of learning, gradually narrowing the scope Neighborhood
Platform: | Size: 6622 | Author: fuyu | Hits:

[Other] BAM_NN

Description: 用外积和法设计的权矩阵，不能保证p对模式全部正确的联想。若对记忆模式对加以限制（即要求p个记忆模式Xk是两两正交的），则用外积和法设计的BAM网具有较好的联想能力。在难以保证要识别的样本（或记忆模式）是正交的情况下，如何求权矩阵，并保证具有较好的联想能力？这个问题在用BAM网络实现对字符的识别程序仿真中得到体现。我们做过尝试，用伪逆法求权矩阵，虽然能对未加干扰的字符全部进行识别，但对加有噪声的字符识别效果很差。至于采用改变结构和其他算法的方法来求权矩阵，将是下一步要做的工作。-foreign plot and the design of the power matrix, p is no guarantee that all the correct pattern association. If memory model, the limit (that is, p-memory model Xk is orthogonal to the February 2), then foreign plot and design of the BAM network has good ability to think. It is difficult to ensure the samples to identify (or memory mode) is orthogonal circumstances, the right to seek ways matrix, and to ensure that the association has good ability? The problem with the BAM network of characters identification procedures simulation can be manifested. We did try to use pseudo - inverse matrix for the right, although they would not increase the interference of the characters in the identification of all, However, a pair of noise increases the effects of poor character recognition. As for the
Platform: | Size: 231686 | Author: 东方云 | Hits:

[Develop Tools] tixingongsi

Description: 梯形公式计算面积近似值:In=Tn=h/2(f(a)+f(b)) 变长梯形面积:T2n=Tn/2+h/2∑f(Xk+h/2) 辛普生面积:I2n=(4T2n-Tn)/3
Platform: | Size: 1002 | Author: 罗小卫 | Hits:

[Internet-Network] tt

Description: 计算xk+1除CRC-CCITT后的余式，k从1到32768
Platform: | Size: 1427 | Author: 小刀 | Hits:

[Crack Hack] crc-ccitt

Description: 计算xk+1除CRC-CCITT后的余式，k从1到32768
Platform: | Size: 2622 | Author: 小刀 | Hits:

[Crack Hack] crc-32

Description: 计算xk+1除CRC-32后的余式，k从1到32768
Platform: | Size: 4092 | Author: 小刀 | Hits:

[assembly language] xk

Description: 数控调频台控制器 *************************** 26H-29H放显示小数位、个位、十位、百位BCD码数，24H-25H放频率控制数据（十六进制）
Platform: | Size: 14845 | Author: a394474295 | Hits:

[Special Effects] henon

Description: Henon吸引子 Henon吸引子是混沌和分形的著名例子,迭代模型为 xk+1=1+ax2k+byk, yk+1= xk. 最常见的参数取值为a=1.4,b=0.3 .此时迭代模型为 xk+1=1+yk-1.4x2k, yk+1=0.3xk.
Platform: | Size: 3023 | Author: 何任飞 | Hits:

[WEB Code] code

Description: 对偶单纯形法的迭代仍然是以为主元的旋转变换，但是它也有自己的特点。它是首先确定离开基的变量，即首先确定然后确定进入基的变量，即确定xk，一下就是求解单纯形法一例
Platform: | Size: 6697 | Author: 蒙仕旺 | Hits:

[CSharp] xk

Description: Microsoft TCP/IP Sockets Development Kit 1.0 for dos & windows 开发包-Microsoft TCP / IP Sockets Development Kit 1.0 for dos windows Development Kit
Platform: | Size: 4965 | Author: xk | Hits:

[Other] 优先suanfa

Description: 操作系统之采用最优分配算法分配xk大小的空间-operating system using optimal allocation algorithm xk size distribution space
Platform: | Size: 7557 | Author: ufo | Hits:

[Documents] ARM嵌入式WINCE实践教程

Description: 密码：xk ARM 嵌入式 WINDOWS CE 实践教程》是 ARM 嵌入式技术系列教程之一。该教程教材采用华中科技大学和武汉创维特信息技术有限公司联合研制的 JXARM9-2410 嵌入式教学实验系统作为教学实践平台。 Windows CE 嵌入式操作系统是目前嵌入式主流操作系统之一，它以超强的性能、友好的 Windows 人机界面以及标准化、可视化的软件开发过程被越来越多地使用在手机、PDA 等产品中，本书结合 ARM9 嵌入式处理器的特点，介绍了在其上进行 Windows CE 开发的过程和方法。本书详细地讲解了 Windows CE BSP 开发平台 Platform Build 4.2 的建立过程、Windows CE BSP 板级支持包的开发、驱动程序的设计以及基于 Embedded Visual C++嵌入式 Windows CE 应用程序的设计和开发等，内容覆盖 Windows CE 开发的全过程，它不仅可以作为嵌入式 Windows CE 开发的初学者作为实践教程，而且对于 Windows CE 开发人员也有一定的参考价值。
Platform: | Size: 5855954 | Author: oskycar | Hits:

[CSharp] xk

Description: Microsoft TCP/IP Sockets Development Kit 1.0 for dos & windows 开发包-Microsoft TCP/IP Sockets Development Kit 1.0 for dos windows Development Kit
Platform: | Size: 5120 | Author: | Hits:

[Other] 优先suanfa

Description: 操作系统之采用最优分配算法分配xk大小的空间-operating system using optimal allocation algorithm xk size distribution space
Platform: | Size: 174080 | Author: ufo | Hits:

[AI-NN-PR] ProSOM

Description: SOM的学习规则有三个主要阶段： 1）寻找与输入模式xk最接近的连接权向量Wj*=（wj*1, wj*2,….., wj*N） 2）将该连接权向量Wj*进一步朝向与输入模式xk接近的方向调整 3）除调整连接权向量Wj*外，还调整邻域内的各个连接权向量，并随着学习次数的增加，逐渐缩小邻域范围-SOM learning rules are three main stages : 1) Find the input mode xk closest to the connection weights vector Wj*= (wj* 1,* 2 wj ... .., wj* N) 2) for the right to connect Vector further towards* Wj with input mode xk close reorientation 3) Subject to adjustment weights Vector* Wj, also adjusted neighborhood in all the right connections Vector, and with the increase in the number of learning, gradually narrowing the scope Neighborhood
Platform: | Size: 6144 | Author: fuyu | Hits:

[Other] BAM_NN

Description: 用外积和法设计的权矩阵，不能保证p对模式全部正确的联想。若对记忆模式对加以限制（即要求p个记忆模式Xk是两两正交的），则用外积和法设计的BAM网具有较好的联想能力。在难以保证要识别的样本（或记忆模式）是正交的情况下，如何求权矩阵，并保证具有较好的联想能力？这个问题在用BAM网络实现对字符的识别程序仿真中得到体现。我们做过尝试，用伪逆法求权矩阵，虽然能对未加干扰的字符全部进行识别，但对加有噪声的字符识别效果很差。至于采用改变结构和其他算法的方法来求权矩阵，将是下一步要做的工作。-foreign plot and the design of the power matrix, p is no guarantee that all the correct pattern association. If memory model, the limit (that is, p-memory model Xk is orthogonal to the February 2), then foreign plot and design of the BAM network has good ability to think. It is difficult to ensure the samples to identify (or memory mode) is orthogonal circumstances, the right to seek ways matrix, and to ensure that the association has good ability? The problem with the BAM network of characters identification procedures simulation can be manifested. We did try to use pseudo- inverse matrix for the right, although they would not increase the interference of the characters in the identification of all, However, a pair of noise increases the effects of poor character recognition. As for the
Platform: | Size: 231424 | Author: 东方云 | Hits:

[Books] tixingongsi

Description: 梯形公式计算面积近似值:In=Tn=h/2(f(a)+f(b)) 变长梯形面积:T2n=Tn/2+h/2∑f(Xk+h/2) 辛普生面积:I2n=(4T2n-Tn)/3 -Trapezoidal area approximation formula: In = Tn = h/2 (f (a)+ F (b)) variable-length trapezoidal area: T2n = Tn/2+ H/2Σf (Xk+ H/2) Simpson area : I2n = (4T2n-Tn)/3
Platform: | Size: 1024 | Author: 罗小卫 | Hits:

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[Other resource] ProSOM

[Other] BAM_NN

[Develop Tools] tixingongsi

[Internet-Network] tt

[Crack Hack] crc-ccitt

[Crack Hack] crc-32

[assembly language] xk

[Special Effects] henon

[WEB Code] code

[CSharp] xk

[Other] 优先suanfa

[Documents] ARM嵌入式WINCE实践教程

[CSharp] xk

[Other] 优先suanfa

[AI-NN-PR] ProSOM

[Other] BAM_NN

[Books] tixingongsi

[Internet-Network] tt

[Crack Hack] crc-ccitt

[Education soft system] xk

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