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堆排序适合于待排序文件中记录较多的情况,因为其主要的时间开销在于建堆和调整堆...
堆排序适合于待排序文件中记录较多的情况,因为其主要的时间开销在于建堆和调整堆-HEAPSORT be suitable for sorting paper records more, because the main overhead is the time to build reactors and adjusting the heap
- 2022-08-15 16:06:56下载
- 积分:1
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遗传算法的文章
遗传算法的文章- Heredity algorithm article
- 2022-06-28 05:43:03下载
- 积分:1
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Plus noise to the voice of a program, you can determine the needs to increase th...
给语音加噪声的一个程序,可以给定需要加噪声的分贝数,程序会自动加载,选用的是随机白噪声。-Plus noise to the voice of a program, you can determine the needs to increase the number of decibels of noise, the program will automatically load, selected at random white noise.
- 2022-04-18 20:02:01下载
- 积分:1
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神经网络与遗传算法在水科学领域的应用…
神经网络和遗传算法在水科学领域的应用-neural networks and genetic algorithms in the field of water science applications
- 2023-01-29 03:05:06下载
- 积分:1
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用C++ Builder的分形程序,包括一个分形生物。
用C++ Builder的分形程序,包括一个分形生物。 -With C++ Builder fractal procedures, including a fractal biological.
- 2022-04-17 02:11:28下载
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a radial network algorithm!
一个径向网络算法程序!-a radial network algorithm!
- 2022-09-30 02:00:02下载
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SVDD的工具箱,可以很好的处理一类分类问题,详见Support Vector Data Description一文...
SVDD的工具箱,可以很好的处理一类分类问题,详见Support Vector Data Description一文-SVDD tools,can be used to process one class classification problem,for more details ,we refer to "Support Vector Data Description"
- 2022-11-12 23:15:03下载
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这个程序是个神经网络提供学习能力的实例,是前一个simpleann的功能加强版。...
这个程序是个神经网络提供学习能力的实例,是前一个simpleann的功能加强版。-this procedure is a neural network learning ability example is the former simpleann an enhanced version of the function.
- 2023-03-20 08:20:03下载
- 积分:1
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我做的的八数码游戏!!学人工智能的用vc编写的
我做的的八数码游戏!!学人工智能的用vc编写的-the eight digital game! ! Using artificial intelligence study prepared by the vc
- 2022-09-07 22:15:02下载
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仿真1:首先把网络温度参数T固定在100,按工作规则共进行1000次状态更新,把这1000次状态转移中网络中的各个状态出现的次数Si(i=1,2,…,16)记录
仿真1:首先把网络温度参数T固定在100,按工作规则共进行1000次状态更新,把这1000次状态转移中网络中的各个状态出现的次数Si(i=1,2,…,16)记录下来 按下式计算各个状态出现的实际频率: Pi=Si/∑i=1,NSi=Si/M 同时按照Bo1tzmann分布计算网络各个状态出现概率的理论值: Q(Ei)=(1/Z)exp(-Ei/T) 仿真2:实施降温方案,重新计算 采用快速降温方案:T(t)= T0/(1+t) T从1000降到0.01,按工作规则更新网络状态 当T=0.01时结束降温,再让T保持在0.01进行1000次状态转移,比较两种概率-a simulation : First of all network parameters temperature T fixed at 100 and, according to the rules for a total of 1000 to update the state, this state of the 1000 network transfer of all states for the number of Si (i = 1, 2, ..., 16) all recorded determined by the formula state-of the actual frequency : Pi = Si/i = 1, NSi = Si/M in accordance with Bo1tzmann distributed computing network of states all probability the theoretical value : Q (Ei) = (1/Z) exp (- Ei/T) Simulation 2 : implementation of cooling, re-using rapid cooling programs : T (t) = T0/(1 t) T dropped to 0.01 from 1000 and, according to the rules updated network state when T = 0.01 at the end of cooling, let T at 0.01 for the 1000 state tran
- 2022-08-06 06:55:50下载
- 积分:1